DOCSLIB.ORG

Evaluating Distributed Word Representations for Capturing Semantics of Biomedical Concepts

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Evaluating Distributed Word Representations for Capturing Semantics of Biomedical Concepts Evaluating distributed word representations for capturing semantics of biomedical concepts Muneeb T H1, Sunil Kumar Sahu1 and Ashish Anand1 1Department of Computer Science and Engineering, Indian Institute of Technology Guwahati, Assam - 781039, India muneeb, sunil.sahu, anand.ashish @iitg.ac.in { } Abstract of the vocabulary and the element at the word in- dex is 1 while the other elements are 0s. Two ma- Recently there is a surge in interest in jor drawbacks with this representation are: first, learning vector representations of words length of the vector is huge and the second, there using huge corpus in unsupervised man- is no notion of similarity between words. The in- ner. Such word vector representations, ability of one-hot vector representation to embody also known as word embedding, have lexico-semantic properties prompted researchers been shown to improve the performance of to develop methods which are based on the notion machine learning models in several NLP that the “similar words appear in similar contexts”. tasks. However efficiency of such repre- These methods can broadly be classified into two sentation has not been systematically eval- categories (Turian et al., 2010), namely, distribu- uated in biomedical domain. In this work tional representation and distributed representa- our aim is to compare the performance tion. Both group of methods works in unsuper- of two state-of-the-art word embedding vised manner with huge corpus. Distributional methods, namely word2vec and GloVe on representations are mainly based on co-occurrence a basic task of reflecting semantic simi- matrix O of words in the vocabulary and their con- larity and relatedness of biomedical con- texts. Here, among other possibilities, contexts cepts. For this, vector representations of can be documents or words within a particular all unique words in the corpus of more window. Each entry Oij in the matrix may indi- than 1 million full-length research arti- cate either frequency of word i in the context j cles in biomedical domain are obtained or simply whether the word i has appeared in the from the two methods. These word vec- context j at least once. Co-occurrence matrix can tors are evaluated for their ability to reflect be designed in variety of ways (Turney and Pan- semantic similarity and semantic related- tel, 2010). The major issue with such methods is ness of word-pairs in a benchmark data size of the matrix O and reducing its size gener- set of manually curated semantic similar ally tends to be computationally very expensive. and related words available at http:// Nevertheless, the requirement of constructing and rxinformatics.umn.edu. We ob- storing the matrix O are always there. The second serve that parameters of these models group of methods are mainly based on language do affect their ability to capture lexico- modeling (Bengio et al., 2003). We discuss more semantic properties and word2vec with about these methods in the section 3. particular language modeling seems to Outside the biomedical domain, this kind of perform better than others. representation has shown significant improvement 1 Introduction in the performance of many NLP tasks. For ex- ample, Turian et al. (2010) have improved the per- One of the crucial step in machine learning (ML) formance of chunking and named entity recogni- based NLP models is how we represent word as tion by using word embedding also as one of the an input to our model. Most of earlier works were features in their CRF model. In one study, Col- treating word as atomic symbol and were assign- lobert et al. (2011) have formulated the NLP tasks ing one hot vector to each word. Length of the of parts of speech tagging, chunking, named entity vector in this representation was equal to the size recognition and semantic role labeling as multi- 158 Proceedings of the 2015 Workshop on Biomedical Natural Language Processing (BioNLP 2015), pages 158–163, Beijing, China, July 30, 2015. c 2015 Association for Computational Linguistics task learning problem. They have shown improve- ter. Authors have shown that including word em- ment in the performance when word vectors are bedding based features has improved the perfor- learned together with other NLP tasks. Socher mance of their classifier. et al. (2012) improved the performance of senti- Faruqui and Dyer (2014) have developed an on- ment analysis task and semantic relation classifi- line suit to analyze and compare different word cation task using recursive neural network. One vector representation models on a variety of tasks. common step among these models is: learning of These tasks include syntactic and semantic rela- word embedding from huge unannotated corpus tions, sentence completion and sentiment analy- like Wikipedia, and later use them as features. sis. In another recent work, Levy et al. (2015) Motivated by the above results, we evaluate have done extensive study on the effect of hyper- performance of the two word embedding mod- parameters of word representation models and els, word2vec (Mikolov et al., 2013a; Mikolov et have shown their influence on the performance on al., 2013b) and GloVe (Pennington et al., 2014) word similarity and analogy tasks. However in for their ability to capture syntactic as well as se- both the studies (Faruqui and Dyer, 2014; Levy mantic properties of words in biomedical domain. et al., 2015) the benchmark datasets available for We have used full-length articles obtained from NLP tasks are not suitable for analyzing vector PubMed Central (PMC) open access subset1 as representations of clinical and biomedical terms. our corpus for learning word embedding. For eval- uation we have used publicly available validated 3 Word Embedding reference dataset (Pakhomov et al., 2010; Ped- ersen et al., 2007) containing semantic similarity As discussed earlier, word embedding or dis- and relatedness scores of around 500 word-pairs. tributed representation is a technique of learn- Our results indicate that the word2vec word em- ing vector representation for all words present in bedding is capturing semantic similarity between the given corpus. The learned vector representa- words better than the GloVe word embedding in tion is generally dense, real-valued and of low- the biomedical domain, whereas for the task of dimension. As contrast to one-hot vector repre- semantic relatedness, there does not seem to be sentation each dimension of the word-vector is any statistical significant difference among differ- supposed to represent a latent feature of lexico- ent word-embeddings. semantic properties of the word. In our work we considered two state of the art word embed- 2 Related Work ding techniques, namely, word2vec and GloVe. Although in literature there exists several word- In a recent study, Minarro-Gim˜ enez´ et al. (2015) embedding techniques (Hinton et al., 1986; Ben- have evaluated the efficiency of word2vec in find- gio et al., 2003; Bengio, 2008; Mnih and Hin- ing clinical relationships such as “may treat”, “has ton, 2009; Collobert et al., 2011), the selected two physiological effect” etc. For this, they have word embedding techniques are very much com- selected the manually curated information from putationally efficient and are considered as state- the National Drug File - Reference Terminology of-the art. We have summarized the basic princi- (NDF-RT) ontology as reference data. They have ples of the two methods in subsequent sections. used several corpora for learning word-vector rep- resentation and compared these different vectors. 3.1 word2vec Model The word-vectors obtained from the largest corpus word2vec generates word vector by two different gave the best result for finding the ”may treat” re- schemes of language modeling: continuous bag lationship with accuracy of 38.78%. The relatively of words (CBOW) and skip-gram (Mikolov et al., poor result obtained for finding different clinical 2013a; Mikolov et al., 2013b). In the CBOW relationships indicates the need for more careful method, the goal is to predict a word given the construction of corpus, design of experiment and surrounding words, whereas in skip-gram, given finding better ways to include domain knowledge. a single word, window or context of words are In another recent study, Nikfarjam et al. (2015) predicted. We can say skip-gram model is op- have described an automatic way to find adverse posite of CBOW model. Both models are neural drug reaction mention in social media such as twit- network based language model and take huge cor- 1http://www.ncbi.nlm.nih.gov/pmc/tools/ftp/ pus as an input and learn vector representation for 159 each words in the corpus. We used freely avail- 566 pairs of UMLS concepts which were man- able word2vec2 tool for our purpose. Apart from ually rated for their semantic similarity and 587 the choice of architecture skip-gram or CBOW, pairs of UMLS concepts for semantic relatedness. word2vec has several parameters including size of We removed all pairs in which at least one word context window, dimension of vector, which effect has less than 10 occurrences in the entire corpus the speed and quality of training. as such words are removed while building vocabu- lary from the corpus. After removing less frequent 3.2 GloVe Model words in both reference sets, we obtain 462 pairs GloVe (Pennington et al., 2014) stands for Global for semantic similarity having 278 unique words, Vectors. In some sense, GloVe can be seen as a and 465 pairs for semantic relatedness having 285 hybrid approach, where it considers global context unique words. In both cases, each concept pair is (by considering co-occurrence matrix) as well as given a score in the range of 0 1600, with higher − local context (such as skip-gram model) of words. score implies similar or more related judgments GloVe try to learn vector for words wx and wy such of manual annotators.
Load more

Recommended publications
  • NVIDIA CEO Jensen Huang to Host AI Pioneers Yoshua Bengio, Geoffrey Hinton and Yann Lecun, and Others, at GTC21
    NVIDIA CEO Jensen Huang to Host AI Pioneers Yoshua Bengio, Geoffrey Hinton and Yann LeCun, and Others, at GTC21 Online Conference to Feature Jensen Huang Keynote and 1,300 Talks from Leaders in Data Center, Networking, Graphics and Autonomous Vehicles NVIDIA today announced that its CEO and founder Jensen Huang will host renowned AI pioneers Yoshua Bengio, Geoffrey Hinton and Yann LeCun at the company’s upcoming technology conference, GTC21, running April 12-16. The event will kick off with a news-filled livestreamed keynote by Huang on April 12 at 8:30 am Pacific. Bengio, Hinton and LeCun won the 2018 ACM Turing Award, known as the Nobel Prize of computing, for breakthroughs that enabled the deep learning revolution. Their work underpins the proliferation of AI technologies now being adopted around the world, from natural language processing to autonomous machines. Bengio is a professor at the University of Montreal and head of Mila - Quebec Artificial Intelligence Institute; Hinton is a professor at the University of Toronto and a researcher at Google; and LeCun is a professor at New York University and chief AI scientist at Facebook. More than 100,000 developers, business leaders, creatives and others are expected to register for GTC, including CxOs and IT professionals focused on data center infrastructure. Registration is free and is not required to view the keynote. In addition to the three Turing winners, major speakers include: Girish Bablani, Corporate Vice President, Microsoft Azure John Bowman, Director of Data Science, Walmart
    [Show full text]
  • On Recurrent and Deep Neural Networks
    On Recurrent and Deep Neural Networks Razvan Pascanu Advisor: Yoshua Bengio PhD Defence Universit´ede Montr´eal,LISA lab September 2014 Pascanu On Recurrent and Deep Neural Networks 1/ 38 Studying the mechanism behind learning provides a meta-solution for solving tasks. Motivation \A computer once beat me at chess, but it was no match for me at kick boxing" | Emo Phillips Pascanu On Recurrent and Deep Neural Networks 2/ 38 Motivation \A computer once beat me at chess, but it was no match for me at kick boxing" | Emo Phillips Studying the mechanism behind learning provides a meta-solution for solving tasks. Pascanu On Recurrent and Deep Neural Networks 2/ 38 I fθ(x) = f (θ; x) ? F I f = arg minθ Θ EEx;t π [d(fθ(x); t)] 2 ∼ Supervised Learing I f :Θ D T F × ! Pascanu On Recurrent and Deep Neural Networks 3/ 38 ? I f = arg minθ Θ EEx;t π [d(fθ(x); t)] 2 ∼ Supervised Learing I f :Θ D T F × ! I fθ(x) = f (θ; x) F Pascanu On Recurrent and Deep Neural Networks 3/ 38 Supervised Learing I f :Θ D T F × ! I fθ(x) = f (θ; x) ? F I f = arg minθ Θ EEx;t π [d(fθ(x); t)] 2 ∼ Pascanu On Recurrent and Deep Neural Networks 3/ 38 Optimization for learning θ[k+1] θ[k] Pascanu On Recurrent and Deep Neural Networks 4/ 38 Neural networks Output neurons Last hidden layer bias = 1 Second hidden layer First hidden layer Input layer Pascanu On Recurrent and Deep Neural Networks 5/ 38 Recurrent neural networks Output neurons Output neurons Last hidden layer bias = 1 bias = 1 Recurrent Layer Second hidden layer First hidden layer Input layer Input layer (b) Recurrent
    [Show full text]
  • Yoshua Bengio and Gary Marcus on the Best Way Forward for AI
    Yoshua Bengio and Gary Marcus on the Best Way Forward for AI Transcript of the 23 December 2019 AI Debate, hosted at Mila Moderated and transcribed by Vincent Boucher, Montreal AI AI DEBATE : Yoshua Bengio | Gary Marcus — Organized by MONTREAL.AI and hosted at Mila, on Monday, December 23, 2019, from 6:30 PM to 8:30 PM (EST) Slides, video, readings and more can be found on the MONTREAL.AI debate webpage. Transcript of the AI Debate Opening Address | Vincent Boucher Good Evening from Mila in Montreal Ladies & Gentlemen, Welcome to the “AI Debate”. I am Vincent Boucher, Founding Chairman of Montreal.AI. Our participants tonight are Professor GARY MARCUS and Professor YOSHUA BENGIO. Professor GARY MARCUS is a Scientist, Best-Selling Author, and Entrepreneur. Professor MARCUS has published extensively in neuroscience, genetics, linguistics, evolutionary psychology and artificial intelligence and is perhaps the youngest Professor Emeritus at NYU. He is Founder and CEO of Robust.AI and the author of five books, including The Algebraic Mind. His newest book, Rebooting AI: Building Machines We Can Trust, aims to shake up the field of artificial intelligence and has been praised by Noam Chomsky, Steven Pinker and Garry Kasparov. Professor YOSHUA BENGIO is a Deep Learning Pioneer. In 2018, Professor BENGIO was the computer scientist who collected the largest number of new citations worldwide. In 2019, he received, jointly with Geoffrey Hinton and Yann LeCun, the ACM A.M. Turing Award — “the Nobel Prize of Computing”. He is the Founder and Scientific Director of Mila — the largest university-based research group in deep learning in the world.
    [Show full text]
  • Hello, It's GPT-2
    Hello, It’s GPT-2 - How Can I Help You? Towards the Use of Pretrained Language Models for Task-Oriented Dialogue Systems Paweł Budzianowski1;2;3 and Ivan Vulic´2;3 1Engineering Department, Cambridge University, UK 2Language Technology Lab, Cambridge University, UK 3PolyAI Limited, London, UK [email protected], [email protected] Abstract (Young et al., 2013). On the other hand, open- domain conversational bots (Li et al., 2017; Serban Data scarcity is a long-standing and crucial et al., 2017) can leverage large amounts of freely challenge that hinders quick development of available unannotated data (Ritter et al., 2010; task-oriented dialogue systems across multiple domains: task-oriented dialogue models are Henderson et al., 2019a). Large corpora allow expected to learn grammar, syntax, dialogue for training end-to-end neural models, which typ- reasoning, decision making, and language gen- ically rely on sequence-to-sequence architectures eration from absurdly small amounts of task- (Sutskever et al., 2014). Although highly data- specific data. In this paper, we demonstrate driven, such systems are prone to producing unre- that recent progress in language modeling pre- liable and meaningless responses, which impedes training and transfer learning shows promise their deployment in the actual conversational ap- to overcome this problem. We propose a task- oriented dialogue model that operates solely plications (Li et al., 2017). on text input: it effectively bypasses ex- Due to the unresolved issues with the end-to- plicit policy and language generation modules. end architectures, the focus has been extended to Building on top of the TransferTransfo frame- retrieval-based models.
    [Show full text]
  • Hierarchical Multiscale Recurrent Neural Networks
    Published as a conference paper at ICLR 2017 HIERARCHICAL MULTISCALE RECURRENT NEURAL NETWORKS Junyoung Chung, Sungjin Ahn & Yoshua Bengio ∗ Département d’informatique et de recherche opérationnelle Université de Montréal {junyoung.chung,sungjin.ahn,yoshua.bengio}@umontreal.ca ABSTRACT Learning both hierarchical and temporal representation has been among the long- standing challenges of recurrent neural networks. Multiscale recurrent neural networks have been considered as a promising approach to resolve this issue, yet there has been a lack of empirical evidence showing that this type of models can actually capture the temporal dependencies by discovering the latent hierarchical structure of the sequence. In this paper, we propose a novel multiscale approach, called the hierarchical multiscale recurrent neural network, that can capture the latent hierarchical structure in the sequence by encoding the temporal dependencies with different timescales using a novel update mechanism. We show some evidence that the proposed model can discover underlying hierarchical structure in the sequences without using explicit boundary information. We evaluate our proposed model on character-level language modelling and handwriting sequence generation. 1 INTRODUCTION One of the key principles of learning in deep neural networks as well as in the human brain is to obtain a hierarchical representation with increasing levels of abstraction (Bengio, 2009; LeCun et al., 2015; Schmidhuber, 2015). A stack of representation layers, learned from the data in a way to optimize the target task, make deep neural networks entertain advantages such as generalization to unseen examples (Hoffman et al., 2013), sharing learned knowledge among multiple tasks, and discovering disentangling factors of variation (Kingma & Welling, 2013).
    [Show full text]
  • I2t2i: Learning Text to Image Synthesis with Textual Data Augmentation
    I2T2I: LEARNING TEXT TO IMAGE SYNTHESIS WITH TEXTUAL DATA AUGMENTATION Hao Dong, Jingqing Zhang, Douglas McIlwraith, Yike Guo Data Science Institute, Imperial College London ABSTRACT of text-to-image synthesis is still far from being solved. Translating information between text and image is a funda- GAN-CLS I2T2I mental problem in artificial intelligence that connects natural language processing and computer vision. In the past few A yellow years, performance in image caption generation has seen sig- school nificant improvement through the adoption of recurrent neural bus parked in networks (RNN). Meanwhile, text-to-image generation begun a parking to generate plausible images using datasets of specific cate- lot. gories like birds and flowers. We’ve even seen image genera- A man tion from multi-category datasets such as the Microsoft Com- swinging a baseball mon Objects in Context (MSCOCO) through the use of gen- bat over erative adversarial networks (GANs). Synthesizing objects home plate. with a complex shape, however, is still challenging. For ex- ample, animals and humans have many degrees of freedom, which means that they can take on many complex shapes. We propose a new training method called Image-Text-Image Fig. 1: Comparing text-to-image synthesis with and without (I2T2I) which integrates text-to-image and image-to-text (im- textual data augmentation. I2T2I synthesizes the human pose age captioning) synthesis to improve the performance of text- and bus color better. to-image synthesis. We demonstrate that I2T2I can generate better multi-categories images using MSCOCO than the state- Recently, both fractionally-strided convolutional and con- of-the-art.
    [Show full text]
  • Yoshua Bengio
    Yoshua Bengio Département d’informatique et recherche opérationnelle, Université de Montréal Canada Research Chair Phone : 514-343-6804 on Statistical Learning Algorithms Fax : 514-343-5834 [email protected] www.iro.umontreal.ca/∼bengioy Titles and Distinctions • Full Professor, Université de Montréal, since 2002. Previously associate professor (1997-2002) and assistant professor (1993-1997). • Canada Research Chair on Statistical Learning Algorithms since 2000 (tier 2 : 2000-2005, tier 1 since 2006). • NSERC-Ubisoft Industrial Research Chair, since 2011. Previously NSERC- CGI Industrial Chair, 2005-2010. • Recipient of the ACFAS Urgel-Archambault 2009 prize (covering physics, ma- thematics, computer science, and engineering). • Fellow, CIFAR (Canadian Institute For Advanced Research), since 2004. Co- director of the CIFAR NCAP program since 2014. • Member of the board of the Neural Information Processing Systems (NIPS) Foundation, since 2010. • Action Editor, Journal of Machine Learning Research (JMLR), Neural Compu- tation, Foundations and Trends in Machine Learning, and Computational Intelligence. Member of the 2012 editor-in-chief nominating committee for JMLR. • Fellow, CIRANO (Centre Inter-universitaire de Recherche en Analyse des Organi- sations), since 1997. • Previously Associate Editor, Machine Learning, IEEE Trans. on Neural Net- works. • Founder (1993) and head of the Laboratoire d’Informatique des Systèmes Adaptatifs (LISA), and the Montreal Institute for Learning Algorithms (MILA), currently including 5 faculty, 40 students, 5 post-docs, 5 researchers on staff, and numerous visitors. This is probably the largest concentration of deep learning researchers in the world (around 60 researchers). • Member of the board of the Centre de Recherches Mathématiques, UdeM, 1999- 2009. Member of the Awards Committee of the Canadian Association for Computer Science (2012-2013).
    [Show full text]
  • Generalized Denoising Auto-Encoders As Generative Models
    Generalized Denoising Auto-Encoders as Generative Models Yoshua Bengio, Li Yao, Guillaume Alain, and Pascal Vincent Departement´ d’informatique et recherche operationnelle,´ Universite´ de Montreal´ Abstract Recent work has shown how denoising and contractive autoencoders implicitly capture the structure of the data-generating density, in the case where the cor- ruption noise is Gaussian, the reconstruction error is the squared error, and the data is continuous-valued. This has led to various proposals for sampling from this implicitly learned density function, using Langevin and Metropolis-Hastings MCMC. However, it remained unclear how to connect the training procedure of regularized auto-encoders to the implicit estimation of the underlying data- generating distribution when the data are discrete, or using other forms of corrup- tion process and reconstruction errors. Another issue is the mathematical justifi- cation which is only valid in the limit of small corruption noise. We propose here a different attack on the problem, which deals with all these issues: arbitrary (but noisy enough) corruption, arbitrary reconstruction loss (seen as a log-likelihood), handling both discrete and continuous-valued variables, and removing the bias due to non-infinitesimal corruption noise (or non-infinitesimal contractive penalty). 1 Introduction Auto-encoders learn an encoder function from input to representation and a decoder function back from representation to input space, such that the reconstruction (composition of encoder and de- coder) is good for training examples. Regularized auto-encoders also involve some form of regu- larization that prevents the auto-encoder from simply learning the identity function, so that recon- struction error will be low at training examples (and hopefully at test examples) but high in general.
    [Show full text]
  • Exposing GAN-Synthesized Faces Using Landmark Locations
    Exposing GAN-synthesized Faces Using Landmark Locations Xin Yang∗, Yuezun Li∗, Honggang Qiy, Siwei Lyu∗ ∗ Computer Science Department, University at Albany, State University of New York, USA y School of Computer and Control Engineering, University of the Chinese Academy of Sciences, China ABSTRACT Unlike previous image/video manipulation methods, realistic Generative adversary networks (GANs) have recently led to highly images are generated completely from random noise through a realistic image synthesis results. In this work, we describe a new deep neural network. Current detection methods are based on low method to expose GAN-synthesized images using the locations of level features such as color disparities [10, 13], or using the whole the facial landmark points. Our method is based on the observa- image as input to a neural network to extract holistic features [19]. tions that the facial parts configuration generated by GAN models In this work, we develop a new GAN-synthesized face detection are different from those of the real faces, due to the lack of global method based on a more semantically meaningful features, namely constraints. We perform experiments demonstrating this phenome- the locations of facial landmark points. This is because the GAN- non, and show that an SVM classifier trained using the locations of synthesized faces exhibit certain abnormality in the facial landmark facial landmark points is sufficient to achieve good classification locations. Specifically, The GAN-based face synthesis algorithm performance for GAN-synthesized faces. can generate face parts (e.g., eyes, nose, skin, and mouth, etc) with a great level of realistic details, yet it does not have an explicit KEYWORDS constraint over the locations of these parts in a face.
    [Show full text]
  • Extracting and Composing Robust Features with Denoising Autoencoders
    Extracting and Composing Robust Features with Denoising Autoencoders Pascal Vincent, Hugo Larochelle, Yoshua Bengio, Pierre-Antoine Manzagol Dept. IRO, Universit´ede Montr´eal C.P. 6128, Montreal, Qc, H3C 3J7, Canada http://www.iro.umontreal.ca/∼lisa Technical Report 1316, February 2008 Abstract Previous work has shown that the difficulties in learning deep genera- tive or discriminative models can be overcome by an initial unsupervised learning step that maps inputs to useful intermediate representations. We introduce and motivate a new training principle for unsupervised learning of a representation based on the idea of making the learned representa- tions robust to partial corruption of the input pattern. This approach can be used to train autoencoders, and these denoising autoencoders can be stacked to initialize deep architectures. The algorithm can be motivated from a manifold learning and information theoretic perspective or from a generative model perspective. Comparative experiments clearly show the surprising advantage of corrupting the input of autoencoders on a pattern classification benchmark suite. 1 Introduction Recent theoretical studies indicate that deep architectures (Bengio & Le Cun, 2007; Bengio, 2007) may be needed to efficiently model complex distributions and achieve better generalization performance on challenging recognition tasks. The belief that additional levels of functional composition will yield increased representational and modeling power is not new (McClelland et al., 1986; Hin- ton, 1989; Utgoff & Stracuzzi, 2002). However, in practice, learning in deep architectures has proven to be difficult. One needs only to ponder the diffi- cult problem of inference in deep directed graphical models, due to “explaining away”. Also looking back at the history of multi-layer neural networks, their difficult optimization (Bengio et al., 2007; Bengio, 2007) has long prevented reaping the expected benefits of going beyond one or two hidden layers.
    [Show full text]
  • The Creation and Detection of Deepfakes: a Survey
    1 The Creation and Detection of Deepfakes: A Survey YISROEL MIRSKY∗, Georgia Institute of Technology and Ben-Gurion University WENKE LEE, Georgia Institute of Technology Generative deep learning algorithms have progressed to a point where it is dicult to tell the dierence between what is real and what is fake. In 2018, it was discovered how easy it is to use this technology for unethical and malicious applications, such as the spread of misinformation, impersonation of political leaders, and the defamation of innocent individuals. Since then, these ‘deepfakes’ have advanced signicantly. In this paper, we explore the creation and detection of deepfakes an provide an in-depth view how these architectures work. e purpose of this survey is to provide the reader with a deeper understanding of (1) how deepfakes are created and detected, (2) the current trends and advancements in this domain, (3) the shortcomings of the current defense solutions, and (4) the areas which require further research and aention. CCS Concepts: •Security and privacy ! Social engineering attacks; Human and societal aspects of security and privacy; •Computing methodologies ! Machine learning; Additional Key Words and Phrases: Deepfake, Deep fake, reenactment, replacement, face swap, generative AI, social engineering, impersonation ACM Reference format: Yisroel Mirsky and Wenke Lee. 2020. e Creation and Detection of Deepfakes: A Survey. ACM Comput. Surv. 1, 1, Article 1 (January 2020), 38 pages. DOI: XX.XXXX/XXXXXXX.XXXXXXX 1 INTRODUCTION A deepfake is content, generated by an articial intelligence, that is authentic in the eyes of a human being. e word deepfake is a combination of the words ‘deep learning’ and ‘fake’ and primarily relates to content generated by an articial neural network, a branch of machine learning.
    [Show full text]
  • Unsupervised Pretraining, Autoencoder and Manifolds
    Unsupervised Pretraining, Autoencoder and Manifolds Christian Herta Outline ● Autoencoders ● Unsupervised pretraining of deep networks with autoencoders ● Manifold-Hypotheses Autoencoder Problems of training of deep neural networks ● stochastic gradient descent + standard algorithm "Backpropagation": vanishing or exploding gradient: "Vanishing Gradient Problem" [Hochreiter 1991] ● only shallow nets are trainable => feature engineering ● for applications (in the past): most only one layer Solutions for training deep nets ● layer wise pretraining (first by [Hin06] with RBM) – with unlabeled data (unsupervised pretraining) ● Restricted Boltzmann Machines (BM) ● Stacked autoencoder ● Contrastive estimation ● more effective optimization – second order methods, like "Hessian free Optimization" ● more carefully initialization + other neuron types (e.g. linear rectified/maxout) + dropout+ more sophisticated momentum (e.g. nesterov momentum); see e.g. [Glo11] Representation Learning ● "Feature Learning" statt "Feature Engineering" ● Multi Task Learning: – learned Features (distributed representations) can be used for different tasks – unsupervised pretraining + supervised finetuning out task A out task B out task C hidden hidden Input from Dumitru Erhan, Yoshua Bengio, Aaron Courville, Pierre-Antoine Manzagol, Pascal Vincent, Samy Bengio; Why Does Unsupervised Pre-training Help Deep Learning? JMLR2010 Layer wise pretraining with autoencoders Autoencoder ● Goal: encoder decoder reconstruction of the input input = output ● different constraints on
    [Show full text]