A State-Of-The-Art Survey on Deep Learning Theory and Architectures
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Synthesizing Images of Humans in Unseen Poses
Synthesizing Images of Humans in Unseen Poses Guha Balakrishnan Amy Zhao Adrian V. Dalca Fredo Durand MIT MIT MIT and MGH MIT [email protected] [email protected] [email protected] [email protected] John Guttag MIT [email protected] Abstract We address the computational problem of novel human pose synthesis. Given an image of a person and a desired pose, we produce a depiction of that person in that pose, re- taining the appearance of both the person and background. We present a modular generative neural network that syn- Source Image Target Pose Synthesized Image thesizes unseen poses using training pairs of images and poses taken from human action videos. Our network sepa- Figure 1. Our method takes an input image along with a desired target pose, and automatically synthesizes a new image depicting rates a scene into different body part and background lay- the person in that pose. We retain the person’s appearance as well ers, moves body parts to new locations and refines their as filling in appropriate background textures. appearances, and composites the new foreground with a hole-filled background. These subtasks, implemented with separate modules, are trained jointly using only a single part details consistent with the new pose. Differences in target image as a supervised label. We use an adversarial poses can cause complex changes in the image space, in- discriminator to force our network to synthesize realistic volving several moving parts and self-occlusions. Subtle details conditioned on pose. We demonstrate image syn- details such as shading and edges should perceptually agree thesis results on three action classes: golf, yoga/workouts with the body’s configuration. -
Automatic Feature Learning Using Recurrent Neural Networks
Predicting purchasing intent: Automatic Feature Learning using Recurrent Neural Networks Humphrey Sheil Omer Rana Ronan Reilly Cardiff University Cardiff University Maynooth University Cardiff, Wales Cardiff, Wales Maynooth, Ireland [email protected] [email protected] [email protected] ABSTRACT influence three out of four major variables that affect profit. Inaddi- We present a neural network for predicting purchasing intent in an tion, merchants increasingly rely on (and pay advertising to) much Ecommerce setting. Our main contribution is to address the signifi- larger third-party portals (for example eBay, Google, Bing, Taobao, cant investment in feature engineering that is usually associated Amazon) to achieve their distribution, so any direct measures the with state-of-the-art methods such as Gradient Boosted Machines. merchant group can use to increase their profit is sorely needed. We use trainable vector spaces to model varied, semi-structured input data comprising categoricals, quantities and unique instances. McKinsey A.T. Kearney Affected by Multi-layer recurrent neural networks capture both session-local shopping intent and dataset-global event dependencies and relationships for user Price management 11.1% 8.2% Yes sessions of any length. An exploration of model design decisions Variable cost 7.8% 5.1% Yes including parameter sharing and skip connections further increase Sales volume 3.3% 3.0% Yes model accuracy. Results on benchmark datasets deliver classifica- Fixed cost 2.3% 2.0% No tion accuracy within 98% of state-of-the-art on one and exceed state-of-the-art on the second without the need for any domain / Table 1: Effect of improving different variables on operating dataset-specific feature engineering on both short and long event profit, from [22]. -
Exploring the Potential of Sparse Coding for Machine Learning
Portland State University PDXScholar Dissertations and Theses Dissertations and Theses 10-19-2020 Exploring the Potential of Sparse Coding for Machine Learning Sheng Yang Lundquist Portland State University Follow this and additional works at: https://pdxscholar.library.pdx.edu/open_access_etds Part of the Applied Mathematics Commons, and the Computer Sciences Commons Let us know how access to this document benefits ou.y Recommended Citation Lundquist, Sheng Yang, "Exploring the Potential of Sparse Coding for Machine Learning" (2020). Dissertations and Theses. Paper 5612. https://doi.org/10.15760/etd.7484 This Dissertation is brought to you for free and open access. It has been accepted for inclusion in Dissertations and Theses by an authorized administrator of PDXScholar. Please contact us if we can make this document more accessible: [email protected]. Exploring the Potential of Sparse Coding for Machine Learning by Sheng Y. Lundquist A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Computer Science Dissertation Committee: Melanie Mitchell, Chair Feng Liu Bart Massey Garrett Kenyon Bruno Jedynak Portland State University 2020 © 2020 Sheng Y. Lundquist Abstract While deep learning has proven to be successful for various tasks in the field of computer vision, there are several limitations of deep-learning models when com- pared to human performance. Specifically, human vision is largely robust to noise and distortions, whereas deep learning performance tends to be brittle to modifi- cations of test images, including being susceptible to adversarial examples. Addi- tionally, deep-learning methods typically require very large collections of training examples for good performance on a task, whereas humans can learn to perform the same task with a much smaller number of training examples. -
FOR IMMEDIATE RELEASE: Tuesday, April 13, 2021 From: Matthew Zeiler, Clarifai Contact: [email protected]
FOR IMMEDIATE RELEASE: Tuesday, April 13, 2021 From: Matthew Zeiler, Clarifai Contact: [email protected] https://www.clarifai.com Clarifai to Deliver AI/ML Algorithms on Palantir's Data Management Platform for U.S. Army's Ground Station Modernization Fort Lee, New Jersey - Clarifai, a leadinG AI lifecycle platform provider for manaGinG unstructured imaGe, video and text data, announced today that it has partnered with Palantir, Inc. (NYSE: PLTR) to deliver its Artificial IntelliGence and Machine LearninG alGorithms on Palantir’s data manaGement platform. This effort is part of the first phase of the U.S. Army’s Ground Station modernization in support of its Tactical IntelliGence TargetinG Access Node (TITAN) proGram. Palantir was awarded a Phase one Project Agreement throuGh an Other Transaction Agreement (OTA) with Consortium ManaGement Group, Inc. (CMG) on behalf of Consortium for Command, Control and Communications in Cyberspace (C5). DurinG the first phase of the initiative, Clarifai is supportinG Palantir by providinG object detection models to facilitate the desiGn of a Ground station prototype solution. “By providinG our Computer Vision AI solution to Palantir, Clarifai is helpinG the Army leveraGe spatial, hiGh altitude, aerial and terrestrial data sources for use in intelliGence and military operations,” said Dr. Matt Zeiler, Clarifai’s CEO. “The partnership will offer a ‘turnkey’ solution that inteGrates data from various sources, includinG commercial and classified sources from space to Ground sensors.” “In order to target with Great precision, we need to be able to see with Great precision, and that’s the objective of this proGram,” said Lieutenant General Robert P. -
Deep Belief Networks for Phone Recognition
Deep Belief Networks for phone recognition Abdel-rahman Mohamed, George Dahl, and Geoffrey Hinton Department of Computer Science University of Toronto {asamir,gdahl,hinton}@cs.toronto.edu Abstract Hidden Markov Models (HMMs) have been the state-of-the-art techniques for acoustic modeling despite their unrealistic independence assumptions and the very limited representational capacity of their hidden states. There are many proposals in the research community for deeper models that are capable of modeling the many types of variability present in the speech generation process. Deep Belief Networks (DBNs) have recently proved to be very effective for a variety of ma- chine learning problems and this paper applies DBNs to acoustic modeling. On the standard TIMIT corpus, DBNs consistently outperform other techniques and the best DBN achieves a phone error rate (PER) of 23.0% on the TIMIT core test set. 1 Introduction A state-of-the-art Automatic Speech Recognition (ASR) system typically uses Hidden Markov Mod- els (HMMs) to model the sequential structure of speech signals, with local spectral variability mod- eled using mixtures of Gaussian densities. HMMs make two main assumptions. The first assumption is that the hidden state sequence can be well-approximated using a first order Markov chain where each state St at time t depends only on St−1. Second, observations at different time steps are as- sumed to be conditionally independent given a state sequence. Although these assumptions are not realistic, they enable tractable decoding and learning even with large amounts of speech data. Many methods have been proposed for relaxing the very strong conditional independence assumptions of standard HMMs (e.g. -
A Survey on Data Collection for Machine Learning a Big Data - AI Integration Perspective
1 A Survey on Data Collection for Machine Learning A Big Data - AI Integration Perspective Yuji Roh, Geon Heo, Steven Euijong Whang, Senior Member, IEEE Abstract—Data collection is a major bottleneck in machine learning and an active research topic in multiple communities. There are largely two reasons data collection has recently become a critical issue. First, as machine learning is becoming more widely-used, we are seeing new applications that do not necessarily have enough labeled data. Second, unlike traditional machine learning, deep learning techniques automatically generate features, which saves feature engineering costs, but in return may require larger amounts of labeled data. Interestingly, recent research in data collection comes not only from the machine learning, natural language, and computer vision communities, but also from the data management community due to the importance of handling large amounts of data. In this survey, we perform a comprehensive study of data collection from a data management point of view. Data collection largely consists of data acquisition, data labeling, and improvement of existing data or models. We provide a research landscape of these operations, provide guidelines on which technique to use when, and identify interesting research challenges. The integration of machine learning and data management for data collection is part of a larger trend of Big data and Artificial Intelligence (AI) integration and opens many opportunities for new research. Index Terms—data collection, data acquisition, data labeling, machine learning F 1 INTRODUCTION E are living in exciting times where machine learning expertise. This problem applies to any novel application that W is having a profound influence on a wide range of benefits from machine learning. -
Memristor-Based Approximated Computation
Memristor-based Approximated Computation Boxun Li1, Yi Shan1, Miao Hu2, Yu Wang1, Yiran Chen2, Huazhong Yang1 1Dept. of E.E., TNList, Tsinghua University, Beijing, China 2Dept. of E.C.E., University of Pittsburgh, Pittsburgh, USA 1 Email: [email protected] Abstract—The cessation of Moore’s Law has limited further architectures, which not only provide a promising hardware improvements in power efficiency. In recent years, the physical solution to neuromorphic system but also help drastically close realization of the memristor has demonstrated a promising the gap of power efficiency between computing systems and solution to ultra-integrated hardware realization of neural net- works, which can be leveraged for better performance and the brain. The memristor is one of those promising devices. power efficiency gains. In this work, we introduce a power The memristor is able to support a large number of signal efficient framework for approximated computations by taking connections within a small footprint by taking the advantage advantage of the memristor-based multilayer neural networks. of the ultra-integration density [7]. And most importantly, A programmable memristor approximated computation unit the nonvolatile feature that the state of the memristor could (Memristor ACU) is introduced first to accelerate approximated computation and a memristor-based approximated computation be tuned by the current passing through itself makes the framework with scalability is proposed on top of the Memristor memristor a potential, perhaps even the best, device to realize ACU. We also introduce a parameter configuration algorithm of neuromorphic computing systems with picojoule level energy the Memristor ACU and a feedback state tuning circuit to pro- consumption [8], [9]. -
Face Recognition: a Convolutional Neural-Network Approach
98 IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 1, JANUARY 1997 Face Recognition: A Convolutional Neural-Network Approach Steve Lawrence, Member, IEEE, C. Lee Giles, Senior Member, IEEE, Ah Chung Tsoi, Senior Member, IEEE, and Andrew D. Back, Member, IEEE Abstract— Faces represent complex multidimensional mean- include fingerprints [4], speech [7], signature dynamics [36], ingful visual stimuli and developing a computational model for and face recognition [8]. Sales of identity verification products face recognition is difficult. We present a hybrid neural-network exceed $100 million [29]. Face recognition has the benefit of solution which compares favorably with other methods. The system combines local image sampling, a self-organizing map being a passive, nonintrusive system for verifying personal (SOM) neural network, and a convolutional neural network. identity. The techniques used in the best face recognition The SOM provides a quantization of the image samples into a systems may depend on the application of the system. We topological space where inputs that are nearby in the original can identify at least two broad categories of face recognition space are also nearby in the output space, thereby providing systems. dimensionality reduction and invariance to minor changes in the image sample, and the convolutional neural network provides for 1) We want to find a person within a large database of partial invariance to translation, rotation, scale, and deformation. faces (e.g., in a police database). These systems typically The convolutional network extracts successively larger features return a list of the most likely people in the database in a hierarchical set of layers. We present results using the [34]. -
CSE 152: Computer Vision Manmohan Chandraker
CSE 152: Computer Vision Manmohan Chandraker Lecture 15: Optimization in CNNs Recap Engineered against learned features Label Convolutional filters are trained in a Dense supervised manner by back-propagating classification error Dense Dense Convolution + pool Label Convolution + pool Classifier Convolution + pool Pooling Convolution + pool Feature extraction Convolution + pool Image Image Jia-Bin Huang and Derek Hoiem, UIUC Two-layer perceptron network Slide credit: Pieter Abeel and Dan Klein Neural networks Non-linearity Activation functions Multi-layer neural network From fully connected to convolutional networks next layer image Convolutional layer Slide: Lazebnik Spatial filtering is convolution Convolutional Neural Networks [Slides credit: Efstratios Gavves] 2D spatial filters Filters over the whole image Weight sharing Insight: Images have similar features at various spatial locations! Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R. Fergus, Y. LeCun Slide: Lazebnik Convolution as a feature extractor Key operations in a CNN Feature maps Rectified Linear Unit (ReLU) Spatial pooling Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Key operations in a CNN Feature maps Spatial pooling Max Non-linearity Convolution (Learned) Input Image Source: R. Fergus, Y. LeCun Slide: Lazebnik Pooling operations • Aggregate multiple values into a single value • Invariance to small transformations • Keep only most important information for next layer • Reduces the size of the next layer • Fewer parameters, faster computations • Observe larger receptive field in next layer • Hierarchically extract more abstract features Key operations in a CNN Feature maps Spatial pooling Non-linearity Convolution (Learned) . Input Image Input Feature Map Source: R. -
Scene Understanding for Mobile Robots Exploiting Deep Learning Techniques
Scene Understanding for Mobile Robots exploiting Deep Learning Techniques José Carlos Rangel Ortiz Instituto Universitario de Investigación Informática Scene Understanding for Mobile Robots exploiting Deep Learning Techniques José Carlos Rangel Ortiz TESIS PRESENTADA PARA ASPIRAR AL GRADO DE DOCTOR POR LA UNIVERSIDAD DE ALICANTE MENCIÓN DE DOCTOR INTERNACIONAL PROGRAMA DE DOCTORADO EN INFORMÁTICA Dirigida por: Dr. Miguel Ángel Cazorla Quevedo Dr. Jesús Martínez Gómez 2017 The thesis presented in this document has been reviewed and approved for the INTERNATIONAL PhD HONOURABLE MENTION I would like to thank the advises and contributions for this thesis of the external reviewers: Professor Eduardo Mario Nebot (University of Sydney) Professor Cristian Iván Pinzón Trejos (Universidad Tecnológica de Panamá) This thesis is licensed under a CC BY-NC-SA International License (Cre- ative Commons AttributionNonCommercial-ShareAlike 4.0 International License). You are free to share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the ma- terial. The licensor cannot revoke these freedoms as long as you follow the license terms. Under the following terms: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. NonCommercial — You may not use the material for commercial purposes. ShareAlike — If you remix, transform, or build upon the material, you must distribute your contributions under the same license as the original. Document by José Carlos Rangel Ortiz. A mis padres. A Tango, Tomás y Julia en su memoria. -
CNN Architectures
Lecture 9: CNN Architectures Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 1 May 2, 2017 Administrative A2 due Thu May 4 Midterm: In-class Tue May 9. Covers material through Thu May 4 lecture. Poster session: Tue June 6, 12-3pm Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 2 May 2, 2017 Last time: Deep learning frameworks Paddle (Baidu) Caffe Caffe2 (UC Berkeley) (Facebook) CNTK (Microsoft) Torch PyTorch (NYU / Facebook) (Facebook) MXNet (Amazon) Developed by U Washington, CMU, MIT, Hong Kong U, etc but main framework of Theano TensorFlow choice at AWS (U Montreal) (Google) And others... Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 3 May 2, 2017 Last time: Deep learning frameworks (1) Easily build big computational graphs (2) Easily compute gradients in computational graphs (3) Run it all efficiently on GPU (wrap cuDNN, cuBLAS, etc) Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 4 May 2, 2017 Last time: Deep learning frameworks Modularized layers that define forward and backward pass Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 5 May 2, 2017 Last time: Deep learning frameworks Define model architecture as a sequence of layers Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 6 May 2, 2017 Today: CNN Architectures Case Studies - AlexNet - VGG - GoogLeNet - ResNet Also.... - NiN (Network in Network) - DenseNet - Wide ResNet - FractalNet - ResNeXT - SqueezeNet - Stochastic Depth Fei-Fei Li & Justin Johnson & Serena Yeung Lecture 9 - 7 May 2, 2017 Review: LeNet-5 [LeCun et al., 1998] Conv filters were 5x5, applied at stride 1 Subsampling (Pooling) layers were 2x2 applied at stride 2 i.e. -
1 Convolution
CS1114 Section 6: Convolution February 27th, 2013 1 Convolution Convolution is an important operation in signal and image processing. Convolution op- erates on two signals (in 1D) or two images (in 2D): you can think of one as the \input" signal (or image), and the other (called the kernel) as a “filter” on the input image, pro- ducing an output image (so convolution takes two images as input and produces a third as output). Convolution is an incredibly important concept in many areas of math and engineering (including computer vision, as we'll see later). Definition. Let's start with 1D convolution (a 1D \image," is also known as a signal, and can be represented by a regular 1D vector in Matlab). Let's call our input vector f and our kernel g, and say that f has length n, and g has length m. The convolution f ∗ g of f and g is defined as: m X (f ∗ g)(i) = g(j) · f(i − j + m=2) j=1 One way to think of this operation is that we're sliding the kernel over the input image. For each position of the kernel, we multiply the overlapping values of the kernel and image together, and add up the results. This sum of products will be the value of the output image at the point in the input image where the kernel is centered. Let's look at a simple example. Suppose our input 1D image is: f = 10 50 60 10 20 40 30 and our kernel is: g = 1=3 1=3 1=3 Let's call the output image h.