Deep Augmentation in Convolutional Neural Networks
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Generative Adversarial Networks (Gans)
Generative Adversarial Networks (GANs) The coolest idea in Machine Learning in the last twenty years - Yann Lecun Generative Adversarial Networks (GANs) 1 / 39 Overview Generative Adversarial Networks (GANs) 3D GANs Domain Adaptation Generative Adversarial Networks (GANs) 2 / 39 Introduction Generative Adversarial Networks (GANs) 3 / 39 Supervised Learning Find deterministic function f: y = f(x), x:data, y:label Generative Adversarial Networks (GANs) 4 / 39 Unsupervised Learning "Most of human and animal learning is unsupervised learning. If intelligence was a cake, unsupervised learning would be the cake, supervised learning would be the icing on the cake, and reinforcement learning would be the cherry on the cake. We know how to make the icing and the cherry, but we do not know how to make the cake. We need to solve the unsupervised learning problem before we can even think of getting to true AI." - Yann Lecun "You cannot predict what you cannot understand" - Anonymous Generative Adversarial Networks (GANs) 5 / 39 Unsupervised Learning More challenging than supervised learning. No label or curriculum. Some NN solutions: Boltzmann machine AutoEncoder Generative Adversarial Networks Generative Adversarial Networks (GANs) 6 / 39 Unsupervised Learning vs Generative Model z = f(x) vs. x = g(z) P(z|x) vs P(x|z) Generative Adversarial Networks (GANs) 7 / 39 Autoencoders Stacked Autoencoders Use data itself as label Generative Adversarial Networks (GANs) 8 / 39 Autoencoders Denosing Autoencoders Generative Adversarial Networks (GANs) 9 / 39 Variational Autoencoder Generative Adversarial Networks (GANs) 10 / 39 Variational Autoencoder Results Generative Adversarial Networks (GANs) 11 / 39 Generative Adversarial Networks Ian Goodfellow et al, "Generative Adversarial Networks", 2014. -
Lecture 10: Recurrent Neural Networks
Lecture 10: Recurrent Neural Networks Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 1 May 4, 2017 Administrative A1 grades will go out soon A2 is due today (11:59pm) Midterm is in-class on Tuesday! We will send out details on where to go soon Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 2 May 4, 2017 Extra Credit: Train Game More details on Piazza by early next week Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 3 May 4, 2017 Last Time: CNN Architectures AlexNet Figure copyright Kaiming He, 2016. Reproduced with permission. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 4 May 4, 2017 Last Time: CNN Architectures Softmax FC 1000 Softmax FC 4096 FC 1000 FC 4096 FC 4096 Pool FC 4096 3x3 conv, 512 Pool 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 Pool Pool 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 3x3 conv, 512 Pool Pool 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256 3x3 conv, 256 Pool Pool 3x3 conv, 128 3x3 conv, 128 3x3 conv, 128 3x3 conv, 128 Pool Pool 3x3 conv, 64 3x3 conv, 64 3x3 conv, 64 3x3 conv, 64 Input Input VGG16 VGG19 GoogLeNet Figure copyright Kaiming He, 2016. Reproduced with permission. Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 10 - 5 May 4, 2017 Last Time: CNN Architectures Softmax FC 1000 Pool 3x3 conv, 64 3x3 conv, 64 3x3 conv, 64 relu 3x3 conv, 64 3x3 conv, 64 F(x) + x 3x3 conv, 64 .. -
Image Retrieval Algorithm Based on Convolutional Neural Network
Advances in Intelligent Systems Research, volume 133 2nd International Conference on Artificial Intelligence and Industrial Engineering (AIIE2016) Image Retrieval Algorithm Based on Convolutional Neural Network Hailong Liu 1, 2, Baoan Li 1, 2, *, Xueqiang Lv 1 and Yue Huang 3 1Beijing Key Laboratory of Internet Culture and Digital Dissemination Research, Beijing Information Science & Technology University, Beijing 100101, China 2Computer School, Beijing Information Science and Technology University, Beijing 100101, China 3Xuanwu Hospital Capital Medical University, 100053, China *Corresponding author Abstract—With the rapid development of computer technology low, can’t meet the needs of people. In order to overcome this and the increasing of multimedia data on the Internet, how to difficulty, the researchers from the image itself, and put quickly find the desired information in the massive data becomes forward the method of image retrieval based on content. The a hot issue. Image retrieval can be used to retrieve similar images, method is to extract the visual features of the image content: and the effect of image retrieval depends on the selection of image color, texture, shape etc., the image database to be detected features to a certain extent. Based on deep learning, through self- samples for similarity matching, retrieval and sample images learning ability of a convolutional neural network to extract more are similar to the image. The main process of the method is the conducive to the high-level semantic feature of image retrieval selection and extraction of features, but there are "semantic using convolutional neural network, and then use the distance gap" [2] between low-level features and high-level semantic metric function similar image. -
Dynamic Factor Graphs for Time Series Modeling
Dynamic Factor Graphs for Time Series Modeling Piotr Mirowski and Yann LeCun Courant Institute of Mathematical Sciences, New York University, 719 Broadway, New York, NY 10003 USA {mirowski,yann}@cs.nyu.edu http://cs.nyu.edu/∼mirowski/ Abstract. This article presents a method for training Dynamic Fac- tor Graphs (DFG) with continuous latent state variables. A DFG in- cludes factors modeling joint probabilities between hidden and observed variables, and factors modeling dynamical constraints on hidden vari- ables. The DFG assigns a scalar energy to each configuration of hidden and observed variables. A gradient-based inference procedure finds the minimum-energy state sequence for a given observation sequence. Be- cause the factors are designed to ensure a constant partition function, they can be trained by minimizing the expected energy over training sequences with respect to the factors’ parameters. These alternated in- ference and parameter updates can be seen as a deterministic EM-like procedure. Using smoothing regularizers, DFGs are shown to reconstruct chaotic attractors and to separate a mixture of independent oscillatory sources perfectly. DFGs outperform the best known algorithm on the CATS competition benchmark for time series prediction. DFGs also suc- cessfully reconstruct missing motion capture data. Key words: factor graphs, time series, dynamic Bayesian networks, re- current networks, expectation-maximization 1 Introduction 1.1 Background Time series collected from real-world phenomena are often an incomplete picture of a complex underlying dynamical process with a high-dimensional state that cannot be directly observed. For example, human motion capture data gives the positions of a few markers that are the reflection of a large number of joint angles with complex kinematic and dynamical constraints. -
LSTM-In-LSTM for Generating Long Descriptions of Images
Computational Visual Media DOI 10.1007/s41095-016-0059-z Vol. 2, No. 4, December 2016, 379–388 Research Article LSTM-in-LSTM for generating long descriptions of images Jun Song1, Siliang Tang1, Jun Xiao1, Fei Wu1( ), and Zhongfei (Mark) Zhang2 c The Author(s) 2016. This article is published with open access at Springerlink.com Abstract In this paper, we propose an approach by means of text (description generation) is a for generating rich fine-grained textual descriptions of fundamental task in artificial intelligence, with many images. In particular, we use an LSTM-in-LSTM (long applications. For example, generating descriptions short-term memory) architecture, which consists of an of images may help visually impaired people better inner LSTM and an outer LSTM. The inner LSTM understand the content of images and retrieve images effectively encodes the long-range implicit contextual using descriptive texts. The challenge of description interaction between visual cues (i.e., the spatially- generation lies in appropriately developing a model concurrent visual objects), while the outer LSTM that can effectively represent the visual cues in generally captures the explicit multi-modal relationship images and describe them in the domain of natural between sentences and images (i.e., the correspondence language at the same time. of sentences and images). This architecture is capable There have been significant advances in of producing a long description by predicting one description generation recently. Some efforts word at every time step conditioned on the previously rely on manually-predefined visual concepts and generated word, a hidden vector (via the outer LSTM), and a context vector of fine-grained visual cues (via sentence templates [1–3]. -
Energy-Based Generative Adversarial Network
Published as a conference paper at ICLR 2017 ENERGY-BASED GENERATIVE ADVERSARIAL NET- WORKS Junbo Zhao, Michael Mathieu and Yann LeCun Department of Computer Science, New York University Facebook Artificial Intelligence Research fjakezhao, mathieu, [email protected] ABSTRACT We introduce the “Energy-based Generative Adversarial Network” model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discrimi- nator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the dis- criminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images. 1I NTRODUCTION 1.1E NERGY-BASED MODEL The essence of the energy-based model (LeCun et al., 2006) is to build a function that maps each point of an input space to a single scalar, which is called “energy”. The learning phase is a data- driven process that shapes the energy surface in such a way that the desired configurations get as- signed low energies, while the incorrect ones are given high energies. -
The History Began from Alexnet: a Comprehensive Survey on Deep Learning Approaches
> REPLACE THIS LINE WITH YOUR PAPER IDENTIFICATION NUMBER (DOUBLE-CLICK HERE TO EDIT) < 1 The History Began from AlexNet: A Comprehensive Survey on Deep Learning Approaches Md Zahangir Alom1, Tarek M. Taha1, Chris Yakopcic1, Stefan Westberg1, Paheding Sidike2, Mst Shamima Nasrin1, Brian C Van Essen3, Abdul A S. Awwal3, and Vijayan K. Asari1 Abstract—In recent years, deep learning has garnered I. INTRODUCTION tremendous success in a variety of application domains. This new ince the 1950s, a small subset of Artificial Intelligence (AI), field of machine learning has been growing rapidly, and has been applied to most traditional application domains, as well as some S often called Machine Learning (ML), has revolutionized new areas that present more opportunities. Different methods several fields in the last few decades. Neural Networks have been proposed based on different categories of learning, (NN) are a subfield of ML, and it was this subfield that spawned including supervised, semi-supervised, and un-supervised Deep Learning (DL). Since its inception DL has been creating learning. Experimental results show state-of-the-art performance ever larger disruptions, showing outstanding success in almost using deep learning when compared to traditional machine every application domain. Fig. 1 shows, the taxonomy of AI. learning approaches in the fields of image processing, computer DL (using either deep architecture of learning or hierarchical vision, speech recognition, machine translation, art, medical learning approaches) is a class of ML developed largely from imaging, medical information processing, robotics and control, 2006 onward. Learning is a procedure consisting of estimating bio-informatics, natural language processing (NLP), cybersecurity, and many others. -
Active Authentication Using an Autoencoder Regularized CNN-Based One-Class Classifier
Active Authentication Using an Autoencoder Regularized CNN-based One-Class Classifier Poojan Oza and Vishal M. Patel Department of Electrical and Computer Engineering, Johns Hopkins University, 3400 N. Charles St, Baltimore, MD 21218, USA email: fpoza2, [email protected] Access Abstract— Active authentication refers to the process in Enrolled User Denied which users are unobtrusively monitored and authenticated Training Images continuously throughout their interactions with mobile devices. AA Access Generally, an active authentication problem is modelled as a Module Granted one class classification problem due to the unavailability of Trained Access data from the impostor users. Normally, the enrolled user is Denied considered as the target class (genuine) and the unauthorized users are considered as unknown classes (impostor). We propose Training a convolutional neural network (CNN) based approach for one class classification in which a zero centered Gaussian noise AA and an autoencoder are used to model the pseudo-negative Module class and to regularize the network to learn meaningful feature Test Images representations for one class data, respectively. The overall network is trained using a combination of the cross-entropy and (a) (b) the reconstruction error losses. A key feature of the proposed approach is that any pre-trained CNN can be used as the Fig. 1: An overview of a typical AA system. (a) Data base network for one class classification. Effectiveness of the corresponding to the enrolled user are used to train an AA proposed framework is demonstrated using three publically system. (b) During testing, data corresponding to the enrolled available face-based active authentication datasets and it is user as well as unknown user may be presented to the system. -
Key Ideas and Architectures in Deep Learning Applications That (Probably) Use DL
Key Ideas and Architectures in Deep Learning Applications that (probably) use DL Autonomous Driving Scene understanding /Segmentation Applications that (probably) use DL WordLens Prisma Outline of today’s talk Image Recognition Fun application using CNNs ● LeNet - 1998 ● Image Style Transfer ● AlexNet - 2012 ● VGGNet - 2014 ● GoogLeNet - 2014 ● ResNet - 2015 Questions to ask about each architecture/ paper Special Layers Non-Linearity Loss function Weight-update rule Train faster? Reduce parameters Reduce Overfitting Help you visualize? LeNet5 - 1998 LeNet5 - Specs MNIST - 60,000 training, 10,000 testing Input is 32x32 image 8 layers 60,000 parameters Few hours to train on a laptop Modified LeNet Architecture - Assignment 3 Training Input Forward pass Maxp Max Conv ReLU Conv ReLU FC ReLU Softmax ool pool Backpropagation - Labels Loss update weights Modified LeNet Architecture - Assignment 3 Testing Input Forward pass Maxp Max Conv ReLU Conv ReLU FC ReLU Softmax ool pool Output Compare output with labels Modified LeNet - CONV Layer 1 Input - 28 x 28 Output - 6 feature maps - each 24 x 24 Convolution filter - 5 x 5 x 1 (convolution) + 1 (bias) How many parameters in this layer? Modified LeNet - CONV Layer 1 Input - 32 x 32 Output - 6 feature maps - each 28 x 28 Convolution filter - 5 x 5 x 1 (convolution) + 1 (bias) How many parameters in this layer? (5x5x1+1)*6 = 156 Modified LeNet - Max-pooling layer Decreases the spatial extent of the feature maps, makes it translation-invariant Input - 28 x 28 x 6 volume Maxpooling with filter size 2 -
Discriminative Recurrent Sparse Auto-Encoders
Discriminative Recurrent Sparse Auto-Encoders Jason Tyler Rolfe & Yann LeCun Courant Institute of Mathematical Sciences, New York University 719 Broadway, 12th Floor New York, NY 10003 frolfe, [email protected] Abstract We present the discriminative recurrent sparse auto-encoder model, comprising a recurrent encoder of rectified linear units, unrolled for a fixed number of itera- tions, and connected to two linear decoders that reconstruct the input and predict its supervised classification. Training via backpropagation-through-time initially minimizes an unsupervised sparse reconstruction error; the loss function is then augmented with a discriminative term on the supervised classification. The depth implicit in the temporally-unrolled form allows the system to exhibit far more representational power, while keeping the number of trainable parameters fixed. From an initially unstructured network the hidden units differentiate into categorical-units, each of which represents an input prototype with a well-defined class; and part-units representing deformations of these prototypes. The learned organization of the recurrent encoder is hierarchical: part-units are driven di- rectly by the input, whereas the activity of categorical-units builds up over time through interactions with the part-units. Even using a small number of hidden units per layer, discriminative recurrent sparse auto-encoders achieve excellent performance on MNIST. 1 Introduction Deep networks complement the hierarchical structure in natural data (Bengio, 2009). By breaking complex calculations into many steps, deep networks can gradually build up complicated decision boundaries or input transformations, facilitate the reuse of common substructure, and explicitly com- pare alternative interpretations of ambiguous input (Lee, Ekanadham, & Ng, 2008; Zeiler, Taylor, arXiv:1301.3775v4 [cs.LG] 19 Mar 2013 & Fergus, 2011). -
Deep Learning in MATLAB
Deep Learning in MATLAB 성 호 현 부장 [email protected] © 2015 The MathWorks, Inc.1 Deep Learning beats Go champion! 2 AI, Machine Learning, and Deep Learning Artificial Machine Deep Learning Intelligence Learning Neural networks with many layers that Any technique Statistical methods learn representations and tasks that enables enable machines to “directly” from data machines to “learn” tasks from data mimic human without explicitly intelligence programming 1950s 1980s 2015 FLOPS Thousand Million Quadrillion 3 What is can Deep Learning do for us? (An example) 4 Example 1: Object recognition using deep learning 5 Object recognition using deep learning Training Millions of images from 1000 (GPU) different categories Real-time object recognition using Prediction a webcam connected to a laptop 6 WhatWhat is isMachine Deep Learning? Learning? 7 Machine Learning vs Deep Learning We specify the nature of the features we want to extract… …and the type of model we want to build. Machine Learning 8 Machine Learning vs Deep Learning We need only specify the architecture of the model… Deep Learning 9 ▪ Deep learning is a type of machine learning in which a model learns to perform tasks like classification – directly from images, texts, or signals. ▪ Deep learning performs end-to-end learning, and is usually implemented using a neural network architecture. ▪ Deep learning algorithms also scale with data – traditional machine learning saturates. 10 Why is Deep Learning So Popular Now? AlexNet Human Accuracy Source: ILSVRC Top-5 Error on ImageNet 11 Two -
Perceptron 1 History of Artificial Neural Networks
Introduction to Machine Learning (Lecture Notes) Perceptron Lecturer: Barnabas Poczos Disclaimer: These notes have not been subjected to the usual scrutiny reserved for formal publications. They may be distributed outside this class only with the permission of the Instructor. 1 History of Artificial Neural Networks The history of artificial neural networks is like a roller-coaster ride. There were times when it was popular(up), and there were times when it wasn't. We are now in one of its very big time. • Progression (1943-1960) { First Mathematical model of neurons ∗ Pitts & McCulloch (1943) [MP43] { Beginning of artificial neural networks { Perceptron, Rosenblatt (1958) [R58] ∗ A single neuron for classification ∗ Perceptron learning rule ∗ Perceptron convergence theorem [N62] • Degression (1960-1980) { Perceptron can't even learn the XOR function [MP69] { We don't know how to train MLP { 1963 Backpropagation (Bryson et al.) ∗ But not much attention • Progression (1980-) { 1986 Backpropagation reinvented: ∗ Learning representations by back-propagation errors. Rumilhart et al. Nature [RHW88] { Successful applications in ∗ Character recognition, autonomous cars, ..., etc. { But there were still some open questions in ∗ Overfitting? Network structure? Neuron number? Layer number? Bad local minimum points? When to stop training? { Hopfield nets (1982) [H82], Boltzmann machines [AHS85], ..., etc. • Degression (1993-) 1 2 Perceptron: Barnabas Poczos { SVM: Support Vector Machine is developed by Vapnik et al.. [CV95] However, SVM is a shallow architecture. { Graphical models are becoming more and more popular { Great success of SVM and graphical models almost kills the ANN (Artificial Neural Network) research. { Training deeper networks consistently yields poor results. { However, Yann LeCun (1998) developed deep convolutional neural networks (a discriminative model).