DOCSLIB.ORG

Energy and Policy Considerations for Deep Learning in NLP

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Energy and Policy Considerations for Deep Learning in NLP Energy and Policy Considerations for Deep Learning in NLP Emma Strubell Ananya Ganesh Andrew McCallum College of Information and Computer Sciences University of Massachusetts Amherst fstrubell, aganesh, [email protected] Abstract Consumption CO2e (lbs) Air travel, 1 passenger, NY$SF 1984 Recent progress in hardware and methodol- Human life, avg, 1 year 11,023 ogy for training neural networks has ushered in a new generation of large networks trained American life, avg, 1 year 36,156 on abundant data. These models have ob- Car, avg incl. fuel, 1 lifetime 126,000 tained notable gains in accuracy across many NLP tasks. However, these accuracy improve- Training one model (GPU) ments depend on the availability of exception- NLP pipeline (parsing, SRL) 31 ally large computational resources that neces- w/ tuning & experimentation 52,909 sitate similarly substantial energy consump- Transformer (big) 192 tion. As a result these models are costly to train and develop, both financially, due to the w/ neural architecture search 626,155 cost of hardware and electricity or cloud com- Table 1: Estimated CO emissions from training com- pute time, and environmentally, due to the car- 2 mon NLP models, compared to familiar consumption.1 bon footprint required to fuel modern tensor processing hardware. In this paper we bring this issue to the attention of NLP researchers NLP models could be trained and developed on by quantifying the approximate financial and environmental costs of training a variety of re- a commodity laptop or server, many now require cently successful neural network models for multiple instances of specialized hardware such as NLP. Based on these findings, we propose ac- GPUs or TPUs, therefore limiting access to these tionable recommendations to reduce costs and highly accurate models on the basis of finances. improve equity in NLP research and practice. Even when these expensive computational re- 1 Introduction sources are available, model training also incurs a substantial cost to the environment due to the en- Advances in techniques and hardware for train- ergy required to power this hardware for weeks or ing deep neural networks have recently en- months at a time. Though some of this energy may abled impressive accuracy improvements across come from renewable or carbon credit-offset re- many fundamental NLP tasks (Bahdanau et al., sources, the high energy demands of these models 2015; Luong et al., 2015; Dozat and Man- are still a concern since (1) energy is not currently ning, 2017; Vaswani et al., 2017), with the derived from carbon-neural sources in many loca- most computationally-hungry models obtaining tions, and (2) when renewable energy is available, the highest scores (Peters et al., 2018; Devlin et al., it is still limited to the equipment we have to pro- 2019; Radford et al., 2019; So et al., 2019). As duce and store it, and energy spent training a neu- a result, training a state-of-the-art model now re- ral network might better be allocated to heating a quires substantial computational resources which family’s home. It is estimated that we must cut demand considerable energy, along with the as- carbon emissions by half over the next decade to sociated financial and environmental costs. Re- deter escalating rates of natural disaster, and based search and development of new models multiplies on the estimated CO2 emissions listed in Table1, these costs by thousands of times by requiring re- 1Sources: (1) Air travel and per-capita consumption: training to experiment with model architectures https://bit.ly/2Hw0xWc; (2) car lifetime: https: and hyperparameters. Whereas a decade ago most //bit.ly/2Qbr0w1. model training and development likely make up Consumer Renew. Gas Coal Nuc. a substantial portion of the greenhouse gas emis- China 22% 3% 65% 4% sions attributed to many NLP researchers. Germany 40% 7% 38% 13% To heighten the awareness of the NLP commu- United States 17% 35% 27% 19% nity to this issue and promote mindful practice and Amazon-AWS 17% 24% 30% 26% policy, we characterize the dollar cost and carbon Google 56% 14% 15% 10% emissions that result from training the neural net- Microsoft 32% 23% 31% 10% works at the core of many state-of-the-art NLP models. We do this by estimating the kilowatts Table 2: Percent energy sourced from: Renewable (e.g. of energy required to train a variety of popular hydro, solar, wind), natural gas, coal and nuclear for off-the-shelf NLP models, which can be converted the top 3 cloud compute providers (Cook et al., 2017), 4 5 to approximate carbon emissions and electricity compared to the United States, China and Germany costs. To estimate the even greater resources re- (Burger, 2019). quired to transfer an existing model to a new task or develop new models, we perform a case study We estimate the total time expected for mod- of the full computational resources required for the els to train to completion using training times and development and tuning of a recent state-of-the-art hardware reported in the original papers. We then NLP pipeline (Strubell et al., 2018). We conclude calculate the power consumption in kilowatt-hours with recommendations to the community based on (kWh) as follows. Let pc be the average power our findings, namely: (1) Time to retrain and sen- draw (in watts) from all CPU sockets during train- sitivity to hyperparameters should be reported for ing, let pr be the average power draw from all NLP machine learning models; (2) academic re- DRAM (main memory) sockets, let pg be the aver- searchers need equitable access to computational age power draw of a GPU during training, and let resources; and (3) researchers should prioritize de- g be the number of GPUs used to train. We esti- veloping efficient models and hardware. mate total power consumption as combined GPU, CPU and DRAM consumption, then multiply this 2 Methods by Power Usage Effectiveness (PUE), which ac- counts for the additional energy required to sup- To quantify the computational and environmen- port the compute infrastructure (mainly cooling). tal cost of training deep neural network mod- We use a PUE coefficient of 1.58, the 2018 global els for NLP, we perform an analysis of the en- average for data centers (Ascierto, 2018). Then the ergy required to train a variety of popular off- total power p required at a given instance during the-shelf NLP models, as well as a case study of t training is given by: the complete sum of resources required to develop LISA (Strubell et al., 2018), a state-of-the-art NLP 1:58t(p + p + gp ) p = c r g (1) model from EMNLP 2018, including all tuning t 1000 and experimentation. The U.S. Environmental Protection Agency (EPA) We measure energy use as follows. We train the provides average CO2 produced (in pounds per models described in x2.1 using the default settings kilowatt-hour) for power consumed in the U.S. provided, and sample GPU and CPU power con- (EPA, 2018), which we use to convert power to sumption during training. Each model was trained estimated CO2 emissions: for a maximum of 1 day. We train all models on a single NVIDIA Titan X GPU, with the excep- CO2e = 0:954pt (2) tion of ELMo which was trained on 3 NVIDIA This conversion takes into account the relative pro- GTX 1080 Ti GPUs. While training, we repeat- portions of different energy sources (primarily nat- edly query the NVIDIA System Management In- ural gas, coal, nuclear and renewable) consumed terface2 to sample the GPU power consumption to produce energy in the United States. Table2 and report the average over all samples. To sample lists the relative energy sources for China, Ger- CPU power consumption, we use Intel’s Running many and the United States compared to the top Average Power Limit interface.3 5U.S. Dept. of Energy: https://bit.ly/2JTbGnI 2nvidia-smi: https://bit.ly/30sGEbi 5China Electricity Council; trans. China Energy Portal: 3RAPL power meter: https://bit.ly/2LObQhV https://bit.ly/2QHE5O3 three cloud service providers. The U.S. break- ence. Devlin et al.(2019) report that the BERT down of energy is comparable to that of the most base model (110M parameters) was trained on 16 popular cloud compute service, Amazon Web Ser- TPU chips for 4 days (96 hours). NVIDIA reports vices, so we believe this conversion to provide a that they can train a BERT model in 3.3 days (79.2 reasonable estimate of CO2 emissions per kilowatt hours) using 4 DGX-2H servers, totaling 64 Tesla hour of compute energy used. V100 GPUs (Forster et al., 2019). GPT-2. This model is the latest edition of 2.1 Models OpenAI’s GPT general-purpose token encoder, We analyze four models, the computational re- also based on Transformer-style self-attention and quirements of which we describe below. All mod- trained with a language modeling objective (Rad- els have code freely available online, which we ford et al., 2019). By training a very large model used out-of-the-box. For more details on the mod- on massive data, Radford et al.(2019) show high els themselves, please refer to the original papers. zero-shot performance on question answering and language modeling benchmarks. The large model Transformer. The Transformer model (Vaswani described in Radford et al.(2019) has 1542M pa- et al., 2017) is an encoder-decoder architecture rameters and is reported to require 1 week (168 primarily recognized for efficient and accurate ma- hours) of training on 32 TPU v3 chips. 6 chine translation. The encoder and decoder each consist of 6 stacked layers of multi-head self- attention.
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]