Notes from the Ai Frontier Insights from Hundreds of Use Cases
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Recurrent Neural Networks
Sequence Modeling: Recurrent and Recursive Nets Lecture slides for Chapter 10 of Deep Learning www.deeplearningbook.org Ian Goodfellow 2016-09-27 Adapted by m.n. for CMPS 392 RNN • RNNs are a family of neural networks for processing sequential data • Recurrent networks can scale to much longer sequences than would be practical for networks without sequence-based specialization. • Most recurrent networks can also process sequences of variable length. • Based on parameter sharing q If we had separate parameters for each value of the time index, o we could not generalize to sequence lengths not seen during training, o nor share statistical strength across different sequence lengths, o and across different positions in time. (Goodfellow 2016) Example • Consider the two sentences “I went to Nepal in 2009” and “In 2009, I went to Nepal.” • How a machine learning can extract the year information? q A traditional fully connected feedforward network would have separate parameters for each input feature, q so it would need to learn all of the rules of the language separately at each position in the sentence. • By comparison, a recurrent neural network shares the same weights across several time steps. (Goodfellow 2016) RNN vs. 1D convolutional • The output of convolution is a sequence where each member of the output is a function of a small number of neighboring members of the input. • Recurrent networks share parameters in a different way. q Each member of the output is a function of the previous members of the output q Each member of the output is produced using the same update rule applied to the previous outputs. -
Ian Goodfellow, Staff Research Scientist, Google Brain CVPR
MedGAN ID-CGAN CoGAN LR-GAN CGAN IcGAN b-GAN LS-GAN LAPGAN DiscoGANMPM-GAN AdaGAN AMGAN iGAN InfoGAN CatGAN IAN LSGAN Introduction to GANs SAGAN McGAN Ian Goodfellow, Staff Research Scientist, Google Brain MIX+GAN MGAN CVPR Tutorial on GANs BS-GAN FF-GAN Salt Lake City, 2018-06-22 GoGAN C-VAE-GAN C-RNN-GAN DR-GAN DCGAN MAGAN 3D-GAN CCGAN AC-GAN BiGAN GAWWN DualGAN CycleGAN Bayesian GAN GP-GAN AnoGAN EBGAN DTN Context-RNN-GAN MAD-GAN ALI f-GAN BEGAN AL-CGAN MARTA-GAN ArtGAN MalGAN Generative Modeling: Density Estimation Training Data Density Function (Goodfellow 2018) Generative Modeling: Sample Generation Training Data Sample Generator (CelebA) (Karras et al, 2017) (Goodfellow 2018) Adversarial Nets Framework D tries to make D(G(z)) near 0, D(x) tries to be G tries to make near 1 D(G(z)) near 1 Differentiable D function D x sampled from x sampled from data model Differentiable function G Input noise z (Goodfellow et al., 2014) (Goodfellow 2018) Self-Play 1959: Arthur Samuel’s checkers agent (OpenAI, 2017) (Silver et al, 2017) (Bansal et al, 2017) (Goodfellow 2018) 3.5 Years of Progress on Faces 2014 2015 2016 2017 (Brundage et al, 2018) (Goodfellow 2018) p.15 General Framework for AI & Security Threats Published as a conference paper at ICLR 2018 <2 Years of Progress on ImageNet Odena et al 2016 monarch butterfly goldfinch daisy redshank grey whale Miyato et al 2017 monarch butterfly goldfinch daisy redshank grey whale Zhang et al 2018 monarch butterfly goldfinch (Goodfellow 2018) daisy redshank grey whale Figure 7: 128x128 pixel images generated by SN-GANs trained on ILSVRC2012 dataset. -
Generative Linguistics and Neural Networks at 60: Foundation, Friction, and Fusion*
Generative linguistics and neural networks at 60: foundation, friction, and fusion* Joe Pater, University of Massachusetts Amherst October 3, 2018. Abstract. The birthdate of both generative linguistics and neural networks can be taken as 1957, the year of the publication of foundational work by both Noam Chomsky and Frank Rosenblatt. This paper traces the development of these two approaches to cognitive science, from their largely autonomous early development in their first thirty years, through their collision in the 1980s around the past tense debate (Rumelhart and McClelland 1986, Pinker and Prince 1988), and their integration in much subsequent work up to the present. Although this integration has produced a considerable body of results, the continued general gulf between these two lines of research is likely impeding progress in both: on learning in generative linguistics, and on the representation of language in neural modeling. The paper concludes with a brief argument that generative linguistics is unlikely to fulfill its promise of accounting for language learning if it continues to maintain its distance from neural and statistical approaches to learning. 1. Introduction At the beginning of 1957, two men nearing their 29th birthdays published work that laid the foundation for two radically different approaches to cognitive science. One of these men, Noam Chomsky, continues to contribute sixty years later to the field that he founded, generative linguistics. The book he published in 1957, Syntactic Structures, has been ranked as the most influential work in cognitive science from the 20th century.1 The other one, Frank Rosenblatt, had by the late 1960s largely moved on from his research on perceptrons – now called neural networks – and died tragically young in 1971. -
Defending Black Box Facial Recognition Classifiers Against Adversarial Attacks
Defending Black Box Facial Recognition Classifiers Against Adversarial Attacks Rajkumar Theagarajan and Bir Bhanu Center for Research in Intelligent Systems, University of California, Riverside, CA 92521 [email protected], [email protected] Abstract attacker has full knowledge about the classification model’s parameters and architecture, whereas in the Black box set- Defending adversarial attacks is a critical step towards ting [50] the attacker does not have this knowledge. In this reliable deployment of deep learning empowered solutions paper we focus on the Black box based adversarial attacks. for biometrics verification. Current approaches for de- Current defenses against adversarial attacks can be clas- fending Black box models use the classification accuracy sified into four approaches: 1) modifying the training data, of the Black box as a performance metric for validating 2) modifying the model, 3) using auxiliary tools, and their defense. However, classification accuracy by itself is 4) detecting and rejecting adversarial examples. Modi- not a reliable metric to determine if the resulting image is fying the training data involves augmenting the training “adversarial-free”. This is a serious problem for online dataset with adversarial examples and re-training the classi- biometrics verification applications where the ground-truth fier [22, 30, 64, 73] or performing N number of pre-selected of the incoming image is not known and hence we cannot image transformations in a random order [13, 17, 26, 52]. compute the accuracy of the classifier or know if the image Modifying the model involves pruning the architecture of is ”adversarial-free” or not. This paper proposes a novel the classifier [38, 51, 69] or adding pre/post-processing lay- framework for defending Black box systems from adversar- ers to it [9, 12, 71]. -
Neural Networks and the Distorted Automation of Intelligence As Statistical Inference Matteo Pasquinelli
JOURNAL > SITE 1: LOGIC GATE, THE POLITICS OF THE ARTIFACTUAL MIND | 2017 Machines that Morph Logic: Neural Networks and the Distorted Automation of Intelligence as Statistical Inference Matteo Pasquinelli Perceptrons [artificial neural networks] are not intended to serve as detailed copies of any actual nervous system. They are simplified networks, designed to permit the study of lawful relationships between the organization of a nerve net, the organization of its environment, and the “psychological” performances of which the network is capable. Perceptrons might actually correspond to parts of more extended networks in biological systems… More likely, they represent extreme simplifications of the central nervous system, in which some properties are exaggerated, others suppressed. —Frank Rosenblatt1 No algorithm exists for the metaphor, nor can a metaphor be produced by means of a computer’s precise instructions, no matter what the volume of organized information to be fed in. —Umberto Eco2 The term Artificial Intelligence is often cited in popular press as well as in art and philosophy circles as an alchemic talisman whose functioning is rarely explained. The hegemonic paradigm to date (also crucial to the automation of labor) is not based on GOFAI (Good Old-Fashioned Artificial Intelligence that never succeeded at automating symbolic deduction), but on the neural networks designed by Frank Rosenblatt back in 1958 to automate statistical induction. The text highlights the role of logic gates in the Machines that Morph Logic: Neural Networks and the Distorted Automation of Intelligence as Statistical Inference | Matteo Pasquinelli distributed architecture of neural networks, in which a generalized control loop affects each node of computation to perform pattern recognition. -
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. -
Ian Goodfellow Deep Learning Pdf
Ian goodfellow deep learning pdf Continue Ian Goodfellow Born1985/1986 (age 34-35)NationalityAmericanAlma materStanford UniversityUniversit de Montr'aKnown for generative competitive networks, Competitive examplesSpopapocomputer scienceInstitutionApple. Google BrainOpenAIThesisDeep Learning Views and Its Application to Computer Vision (2014)Doctoral Adviser Yashua BengioAron Kurville Websitewww.iangoodfellow.com Jan J. Goodfellow (born 1985 or 1986) is a machine learning researcher who currently works at Apple Inc. as director of machine learning in a special project group. Previously, he worked as a researcher at Google Brain. He has made a number of contributions to the field of deep learning. Biography Of Goodfellow received a bachelor's and doctorate in computer science from Stanford University under the direction of Andrew Ng and a PhD in Machine Learning from the University of Montreal in April 2014 under the direction of Joshua Bengio and Aaron Kurwill. His dissertation is called Deep Study of Representations and its application to computer vision. After graduating from university, Goodfellow joined Google as part of the Google Brain research team. He then left Google to join the newly founded OpenAI Institute. He returned to Google Research in March 2017. Goodfellow is best known for inventing generative adversarial networks. He is also the lead author of the Deep Learning textbook. At Google, he developed a system that allows Google Maps to automatically transcribe addresses from photos taken by Street View cars, and demonstrated the vulnerabilities of machine learning systems. In 2017, Goodfellow was mentioned in 35 innovators under the age of 35 at MIT Technology Review. In 2019, it was included in the list of 100 global thinkers Foreign Policy. -
Semi-Supervised Neural Architecture Search
Semi-Supervised Neural Architecture Search 1Renqian Luo,∗2Xu Tan, 2Rui Wang, 2Tao Qin, 1Enhong Chen, 2Tie-Yan Liu 1University of Science and Technology of China, Hefei, China 2Microsoft Research Asia, Beijing, China [email protected], [email protected] 2{xuta, ruiwa, taoqin, tyliu}@microsoft.com Abstract Neural architecture search (NAS) relies on a good controller to generate better architectures or predict the accuracy of given architectures. However, training the controller requires both abundant and high-quality pairs of architectures and their accuracy, while it is costly to evaluate an architecture and obtain its accuracy. In this paper, we propose SemiNAS, a semi-supervised NAS approach that leverages numerous unlabeled architectures (without evaluation and thus nearly no cost). Specifically, SemiNAS 1) trains an initial accuracy predictor with a small set of architecture-accuracy data pairs; 2) uses the trained accuracy predictor to predict the accuracy of large amount of architectures (without evaluation); and 3) adds the generated data pairs to the original data to further improve the predictor. The trained accuracy predictor can be applied to various NAS algorithms by predicting the accuracy of candidate architectures for them. SemiNAS has two advantages: 1) It reduces the computational cost under the same accuracy guarantee. On NASBench-101 benchmark dataset, it achieves comparable accuracy with gradient- based method while using only 1/7 architecture-accuracy pairs. 2) It achieves higher accuracy under the same computational cost. It achieves 94.02% test accuracy on NASBench-101, outperforming all the baselines when using the same number of architectures. On ImageNet, it achieves 23.5% top-1 error rate (under 600M FLOPS constraint) using 4 GPU-days for search. -
Amora: Black-Box Adversarial Morphing Attack
Amora: Black-box Adversarial Morphing Attack Run Wang1, Felix Juefei-Xu2, Qing Guo1,y, Yihao Huang3, Xiaofei Xie1, Lei Ma4, Yang Liu1,5 1Nanyang Technological University, Singapore 2Alibaba Group, USA 3East China Normal University, China 4Kyushu University, Japan 5Institute of Computing Innovation, Zhejiang University, China ABSTRACT Nowadays, digital facial content manipulation has become ubiq- uitous and realistic with the success of generative adversarial net- works (GANs), making face recognition (FR) systems suffer from unprecedented security concerns. In this paper, we investigate and introduce a new type of adversarial attack to evade FR systems by (a) Presentation spoofing attacks manipulating facial content, called adversarial morphing attack (a.k.a. Amora). In contrast to adversarial noise attack that perturbs pixel intensity values by adding human-imperceptible noise, our proposed adversarial morphing attack works at the semantic level that perturbs pixels spatially in a coherent manner. To tackle the (b) Adversarial noise attack black-box attack problem, we devise a simple yet effective joint dictionary learning pipeline to obtain a proprietary optical flow field for each attack. Our extensive evaluation on two popular FR systems demonstrates the effectiveness of our adversarial morphing attack at various levels of morphing intensity with smiling facial ex- pression manipulations. Both open-set and closed-set experimental (c) Adversarial morphing attack results indicate that a novel black-box adversarial attack based on local deformation is possible, and is vastly different from additive Figure 1: Three typical attacks on the FR systems. (a) presentation spoofing at- noise attacks. The findings of this work potentially pave anew tacks (e.g., print attack [14], disguise attack [50], mask attack [36], and replay research direction towards a more thorough understanding and attack [8]), (b) adversarial noise attack [14, 18, 42], (c) proposed Amora. -
CS 2770: Computer Vision Generative Adversarial Networks
CS 2770: Computer Vision Generative Adversarial Networks Prof. Adriana Kovashka University of Pittsburgh April 14, 2020 Plan for this lecture • Generative models: What are they? • Technique: Generative Adversarial Networks • Applications • Conditional GANs • Cycle-consistency loss • Dealing with sparse data, progressive training Supervised vs Unsupervised Learning Unsupervised Learning Data: x Just data, no labels! Goal: Learn some underlying hidden structure of the data Examples: Clustering, K-means clustering dimensionality reduction, feature learning, density estimation, etc. Lecture 13 - 10 Fei-Fei Li & Justin Johnson & May 18, 2017 SerenaSerena YoungYeung Supervised vs Unsupervised Learning Unsupervised Learning Data: x Just data, no labels! Goal: Learn some underlying hidden structure of the data Examples: Clustering, dimensionality reduction, feature Autoencoders learning, density estimation, etc. (Feature learning) Lecture 13 - Fei-Fei Li & Justin Johnson & May 18, 2017 SerenaSerena YoungYeung Generative Models Training data ~ pdata(x) Generated samples ~ pmodel(x) Want to learn pmodel(x) similar to pdata(x) Lecture 13 - Fei-Fei Li & Justin Johnson & May 18, 2017 SerenaSerena YoungYeung Generative Models Training data ~ pdata(x) Generated samples ~ pmodel(x) Want to learn pmodel(x) similar to pdata(x) Addresses density estimation, a core problem in unsupervised learning Several flavors: - Explicit density estimation: explicitly define and solve for pmodel(x) - Implicit density estimation: learn model that can sample from pmodel(x) -
Practical Methodology
Practical Methodology Lecture slides for Chapter 11 of Deep Learning www.deeplearningbook.org Ian Goodfellow 2016-09-26 What drives success in ML? What drives success in ML? Arcane knowledge Knowing how Mountains of dozens of to apply 3-4 of data? obscure algorithms? standard techniques? (2) (2) (2) h1 h2 h3 (1) (1) (1) (1) h1 h2 h3 h4 v1 v2 v3 (Goodfellow 2016) Example: Street View Address StreetNumber View Transcription Transcription (Goodfellow et al, 2014) (Goodfellow 2016) 3 Step Process Three Step Process • Use needs to define metric-based goals • Build an end-to-end system • Data-driven refinement (Goodfellow 2016) Identify needs Identify Needs • High accuracy or low accuracy? • Surgery robot: high accuracy • Celebrity look-a-like app: low accuracy (Goodfellow 2016) ChooseChoose Metrics Metrics • Accuracy? (% of examples correct) • Coverage? (% of examples processed) • Precision? (% of detections that are right) • Recall? (% of objects detected) • Amount of error? (For regression problems) (Goodfellow 2016) End-to-endEnd-to-end System system • Get up and running ASAP • Build the simplest viable system first • What baseline to start with though? • Copy state-of-the-art from related publication (Goodfellow 2016) DeepDeep or or Not? not? • Lots of noise, little structure -> not deep • Little noise, complex structure -> deep • Good shallow baseline: • Use what you know • Logistic regression, SVM, boosted tree are all good (Goodfellow 2016) What kind of deep? Choosing Architecture Family • No structure -> fully connected • Spatial structure -
Convolutional Neural Networks • Development • Application • Architecture • Optimization 2
Convolutionary Neural Networks Lingzhou Xue Penn State Department of Statistics [email protected] http://sites.psu.edu/sldm/deeplearning/ Materials • C. Lee Giles (Penn State) • http://clgiles.ist.psu.edu/IST597/index.html • Andrew Ng (Coursera, Stanford U., Google Brain, Biadu) • https://www.deeplearning.ai/ • Fei-Fei Li (Stanford U., Google Cloud) • http://vision.stanford.edu/teaching.html • Aaron Courville (U Montreal), Ian Goodfellow (Google Brain), and Yoshua Bengio (U Montreal) • http://www.deeplearningbook.org/ Outline 1. Convolutional Neural Networks • Development • Application • Architecture • Optimization 2. Deep Learning Software 3. TensorFlow Demos 1. Convolutional Neural Networks Convolutional Neural Networks (CNNs/ConvNets) • Convolution: a specialized type of linear operation • CNNs: a specialized type of neural network using the convolution (instead of general matrix multiplication) in at least one of its layers • CNNs are biologically-inspired to emulate the animal visual cortex Biological Motivation and Connections The dendrites in biological neurons perform complex nonlinear computations. The synapses are not just a single weight, they’re a complex non-linear system. The activation function takes the decision of whether or not to pass the signal. Activation Functions: Sigmoid/Logistic and Tanh • (-) Gradients at tails are almost zero (vanishing gradients) • (-) Outputs (y-axis) are not zero-centered for Sigmoid/Logistic • (-) Computationally expensive Rectified Linear Unit (ReLU) • (+) Accelerate the convergence of SGD compared to sigmoid/tanh • (+) Easy to implement by simply thresholding at zero • First introduced by Hahnloser et al. (Nature, 2000) with strong biological motivations and mathematical justifications • First demonstrated in 2011 to enable better training of deeper neural networks • The most popular activation as of 2018.