Deep Convolutional Neural Networks for Tiny Imagenet Classification
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Backpropagation and Deep Learning in the Brain
Backpropagation and Deep Learning in the Brain Simons Institute -- Computational Theories of the Brain 2018 Timothy Lillicrap DeepMind, UCL With: Sergey Bartunov, Adam Santoro, Jordan Guerguiev, Blake Richards, Luke Marris, Daniel Cownden, Colin Akerman, Douglas Tweed, Geoffrey Hinton The “credit assignment” problem The solution in artificial networks: backprop Credit assignment by backprop works well in practice and shows up in virtually all of the state-of-the-art supervised, unsupervised, and reinforcement learning algorithms. Why Isn’t Backprop “Biologically Plausible”? Why Isn’t Backprop “Biologically Plausible”? Neuroscience Evidence for Backprop in the Brain? A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: A spectrum of credit assignment algorithms: How to convince a neuroscientist that the cortex is learning via [something like] backprop - To convince a machine learning researcher, an appeal to variance in gradient estimates might be enough. - But this is rarely enough to convince a neuroscientist. - So what lines of argument help? How to convince a neuroscientist that the cortex is learning via [something like] backprop - What do I mean by “something like backprop”?: - That learning is achieved across multiple layers by sending information from neurons closer to the output back to “earlier” layers to help compute their synaptic updates. How to convince a neuroscientist that the cortex is learning via [something like] backprop 1. Feedback connections in cortex are ubiquitous and modify the -
Intelligent Recognition Method of Low-Altitude Squint Optical Ship Target Fused with Simulation Samples
remote sensing Article Intelligent Recognition Method of Low-Altitude Squint Optical Ship Target Fused with Simulation Samples Bo Liu 1,2 , Qi Xiao 1,2, Yuhao Zhang 1,2, Wei Ni 1, Zhen Yang 1 and Ligang Li 1,* 1 National Space Science Center, Key Laboratory of Electronics and Information Technology for Space System, Chinese Academy of Sciences, Beijing 100190, China; [email protected] (B.L.); [email protected] (Q.X.); [email protected] (Y.Z.); [email protected] (W.N.); [email protected] (Z.Y.) 2 School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China * Correspondence: [email protected]; Tel.: +86-1312-152-1820 Abstract: To address the problem of intelligent recognition of optical ship targets under low-altitude squint detection, we propose an intelligent recognition method based on simulation samples. This method comprehensively considers geometric and spectral characteristics of ship targets and ocean background and performs full link modeling combined with the squint detection atmospheric transmission model. It also generates and expands squint multi-angle imaging simulation samples of ship targets in the visible light band using the expanded sample type to perform feature analysis and modification on SqueezeNet. Shallow and deeper features are combined to improve the accuracy of feature recognition. The experimental results demonstrate that using simulation samples to expand the training set can improve the performance of the traditional k-nearest neighbors algorithm and modified SqueezeNet. For the classification of specific ship target types, a mixed-scene dataset Citation: Liu, B.; Xiao, Q.; Zhang, Y.; expanded with simulation samples was used for training. -
Automated Elastic Pipelining for Distributed Training of Transformers
PipeTransformer: Automated Elastic Pipelining for Distributed Training of Transformers Chaoyang He 1 Shen Li 2 Mahdi Soltanolkotabi 1 Salman Avestimehr 1 Abstract the-art convolutional networks ResNet-152 (He et al., 2016) and EfficientNet (Tan & Le, 2019). To tackle the growth in The size of Transformer models is growing at an model sizes, researchers have proposed various distributed unprecedented rate. It has taken less than one training techniques, including parameter servers (Li et al., year to reach trillion-level parameters since the 2014; Jiang et al., 2020; Kim et al., 2019), pipeline paral- release of GPT-3 (175B). Training such models lel (Huang et al., 2019; Park et al., 2020; Narayanan et al., requires both substantial engineering efforts and 2019), intra-layer parallel (Lepikhin et al., 2020; Shazeer enormous computing resources, which are luxu- et al., 2018; Shoeybi et al., 2019), and zero redundancy data ries most research teams cannot afford. In this parallel (Rajbhandari et al., 2019). paper, we propose PipeTransformer, which leverages automated elastic pipelining for effi- T0 (0% trained) T1 (35% trained) T2 (75% trained) T3 (100% trained) cient distributed training of Transformer models. In PipeTransformer, we design an adaptive on the fly freeze algorithm that can identify and freeze some layers gradually during training, and an elastic pipelining system that can dynamically Layer (end of training) Layer (end of training) Layer (end of training) Layer (end of training) Similarity score allocate resources to train the remaining active layers. More specifically, PipeTransformer automatically excludes frozen layers from the Figure 1. Interpretable Freeze Training: DNNs converge bottom pipeline, packs active layers into fewer GPUs, up (Results on CIFAR10 using ResNet). -
NXP Eiq Machine Learning Software Development Environment For
AN12580 NXP eIQ™ Machine Learning Software Development Environment for QorIQ Layerscape Applications Processors Rev. 3 — 12/2020 Application Note Contents 1 Introduction 1 Introduction......................................1 Machine Learning (ML) is a computer science domain that has its roots in 2 NXP eIQ software introduction........1 3 Building eIQ Components............... 2 the 1960s. ML provides algorithms capable of finding patterns and rules in 4 OpenCV getting started guide.........3 data. ML is a category of algorithm that allows software applications to become 5 Arm Compute Library getting started more accurate in predicting outcomes without being explicitly programmed. guide................................................7 The basic premise of ML is to build algorithms that can receive input data and 6 TensorFlow Lite getting started guide use statistical analysis to predict an output while updating output as new data ........................................................ 8 becomes available. 7 Arm NN getting started guide........10 8 ONNX Runtime getting started guide In 2010, the so-called deep learning started. It is a fast-growing subdomain ...................................................... 16 of ML, based on Neural Networks (NN). Inspired by the human brain, 9 PyTorch getting started guide....... 17 deep learning achieved state-of-the-art results in various tasks; for example, A Revision history.............................17 Computer Vision (CV) and Natural Language Processing (NLP). Neural networks are capable of learning complex patterns from millions of examples. A huge adaptation is expected in the embedded world, where NXP is the leader. NXP created eIQ machine learning software for QorIQ Layerscape applications processors, a set of ML tools which allows developing and deploying ML applications on the QorIQ Layerscape family of devices. -
Convolutional Neural Network Transfer Learning for Robust Face
Convolutional Neural Network Transfer Learning for Robust Face Recognition in NAO Humanoid Robot Daniel Bussey1, Alex Glandon2, Lasitha Vidyaratne2, Mahbubul Alam2, Khan Iftekharuddin2 1Embry-Riddle Aeronautical University, 2Old Dominion University Background Research Approach Results • Artificial Neural Networks • Retrain AlexNet on the CASIA-WebFace dataset to configure the neural • VGG-Face Shows better results in every performance benchmark measured • Computing systems whose model architecture is inspired by biological networf for face recognition tasks. compared to AlexNet, although AlexNet is able to extract features from an neural networks [1]. image 800% faster than VGG-Face • Capable of improving task performance without task-specific • Acquire an input image using NAO’s camera or a high resolution camera to programming. run through the convolutional neural network. • Resolution of the input image does not have a statistically significant impact • Show excellent performance at classification based tasks such as face on the performance of VGG-Face and AlexNet. recognition [2], text recognition [3], and natural language processing • Extract the features of the input image using the neural networks AlexNet [4]. and VGG-Face • AlexNet’s performance decreases with respect to distance from the camera where VGG-Face shows no performance loss. • Deep Learning • Compare the features of the input image to the features of each image in the • A subfield of machine learning that focuses on algorithms inspired by people database. • Both frameworks show excellent performance when eliminating false the function and structure of the brain called artificial neural networks. positives. The method used to allow computers to learn through a process called • Determine whether a match is detected or if the input image is a photo of a training. -
Tiny Imagenet Challenge
Tiny ImageNet Challenge Jiayu Wu Qixiang Zhang Guoxi Xu Stanford University Stanford University Stanford University [email protected] [email protected] [email protected] Abstract Our best model, a fine-tuned Inception-ResNet, achieves a top-1 error rate of 43.10% on test dataset. Moreover, we We present image classification systems using Residual implemented an object localization network based on a Network(ResNet), Inception-Resnet and Very Deep Convo- RNN with LSTM [7] cells, which achieves precise results. lutional Networks(VGGNet) architectures. We apply data In the Experiments and Evaluations section, We will augmentation, dropout and other regularization techniques present thorough analysis on the results, including per-class to prevent over-fitting of our models. What’s more, we error analysis, intermediate output distribution, the impact present error analysis based on per-class accuracy. We of initialization, etc. also explore impact of initialization methods, weight decay and network depth on system performance. Moreover, visu- 2. Related Work alization of intermediate outputs and convolutional filters are shown. Besides, we complete an extra object localiza- Deep convolutional neural networks have enabled the tion system base upon a combination of Recurrent Neural field of image recognition to advance in an unprecedented Network(RNN) and Long Short Term Memroy(LSTM) units. pace over the past decade. Our best classification model achieves a top-1 test error [10] introduces AlexNet, which has 60 million param- rate of 43.10% on the Tiny ImageNet dataset, and our best eters and 650,000 neurons. The model consists of five localization model can localize with high accuracy more convolutional layers, and some of them are followed by than 1 objects, given training images with 1 object labeled. -
Fast Inference of Deep Neural Networks in Fpgas for Particle Physics (Arxiv:1804.06913)
Fast Inference of Deep Neural Networks in FPGAs for Particle Physics (arXiv:1804.06913) Jennifer Ngadiuba, Maurizio Pierini [CERN] Javier Duarte, Sergo Jindariani, Ben Kreis, Ryan Rivera, Nhan Tran [Fermilab] Edward Kreinar [Hawkeye 360] Song Han, Phil Harris [MIT] Zhenbin Wu [University of Illinois at Chicago] Research Techniques Seminar Fermilab 4/24/2018 1 Outline • Introduction and Motivation • Machine Learning in HEP • FPGAs and High-Level Synthesis (HLS) • Industry Trends • HEP Latency Landscape • hls4ml: HLS for Machine Learning • Case Study and Design Exploration • Summary and Outlook • Cloud-scale Acceleration Javier Duarte I hls4ml 2 Introduction Javier Duarte I hls4ml 3 ReconstructionMachine chain:Learning Jet tagging in HEP Task to find the particle ID of a jet, e.g. b-quark • Learning optimized nonlinear functions of many inputs for … performingvertices difficult tasks from (real or simulated) data … Many successes in HEP: identificationData/MC of b-quarkuser jets, Higgs • Tag Info tagger corrections … candidates,Jet, particle energy regression, analysis selection, … particles CMS-PAS-BTV-15-001 s=13 TeV, 2016 1 CMS Simulation Preliminary Key features:tt events AK4jets (p > 30 GeV) T • Long lifetimeCSVv2 of heavy 10−1 flavour quarksDeepCSV cMVAv2 • Displacedmisid. probability tracks, … • Usage10−2 of ML standard for this problem udsg Neural network based on 10−3 • 11 c high-level features 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 b-jet efficiency Javier Duarte I hls4ml 4 Machine Learning in HEP • Learning optimized nonlinear functions -
Deep Learning Is Robust to Massive Label Noise
Deep Learning is Robust to Massive Label Noise David Rolnick * 1 Andreas Veit * 2 Serge Belongie 2 Nir Shavit 3 Abstract Thus, annotation can be expensive and, for tasks requiring expert knowledge, may simply be unattainable at scale. Deep neural networks trained on large supervised datasets have led to impressive results in image To address this limitation, other training paradigms have classification and other tasks. However, well- been investigated to alleviate the need for expensive an- annotated datasets can be time-consuming and notations, such as unsupervised learning (Le, 2013), self- expensive to collect, lending increased interest to supervised learning (Pinto et al., 2016; Wang & Gupta, larger but noisy datasets that are more easily ob- 2015) and learning from noisy annotations (Joulin et al., tained. In this paper, we show that deep neural net- 2016; Natarajan et al., 2013; Veit et al., 2017). Very large works are capable of generalizing from training datasets (e.g., Krasin et al.(2016); Thomee et al.(2016)) data for which true labels are massively outnum- can often be obtained, for example from web sources, with bered by incorrect labels. We demonstrate remark- partial or unreliable annotation. This can allow neural net- ably high test performance after training on cor- works to be trained on a much wider variety of tasks or rupted data from MNIST, CIFAR, and ImageNet. classes and with less manual effort. The good performance For example, on MNIST we obtain test accuracy obtained from these large, noisy datasets indicates that deep above 90 percent even after each clean training learning approaches can tolerate modest amounts of noise example has been diluted with 100 randomly- in the training set. -
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices 1st Chunjie Luo 2nd Xiwen He 3nd Jianfeng Zhan Institute of Computing Technology Institute of Computing Technology Institute of Computing Technology Chinese Academy of Sciences Chinese Academy of Sciences Chinese Academy of Sciences BenchCouncil BenchCouncil BenchCouncil Beijing, China Beijing, China Beijing, China [email protected] [email protected] [email protected] 4nd Lei Wang 5nd Wanling Gao 6nd Jiahui Dai Institute of Computing Technology Institute of Computing Technology Beijing Academy of Frontier Chinese Academy of Sciences Chinese Academy of Sciences Sciences and Technology BenchCouncil BenchCouncil Beijing, China Beijing, China Beijing, China [email protected] wanglei [email protected] [email protected] Abstract—Due to increasing amounts of data and compute inference on the edge devices can 1) reduce the latency, 2) resources, deep learning achieves many successes in various protect the privacy, 3) consume the power [2]. domains. The application of deep learning on the mobile and em- To make the inference on the edge devices more efficient, bedded devices is taken more and more attentions, benchmarking and ranking the AI abilities of mobile and embedded devices the neural networks are designed more light-weight by using becomes an urgent problem to be solved. Considering the model simpler architecture, or by quantizing, pruning and compress- diversity and framework diversity, we propose a benchmark suite, ing the networks. Different networks present different trade- AIoTBench, which focuses on the evaluation of the inference offs between accuracy and computational complexity. These abilities of mobile and embedded devices. -
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. -
Kernel Descriptors for Visual Recognition
Kernel Descriptors for Visual Recognition Liefeng Bo University of Washington Seattle WA 98195, USA Xiaofeng Ren Dieter Fox Intel Labs Seattle University of Washington & Intel Labs Seattle Seattle WA 98105, USA Seattle WA 98195 & 98105, USA Abstract The design of low-level image features is critical for computer vision algorithms. Orientation histograms, such as those in SIFT [16] and HOG [3], are the most successful and popular features for visual object and scene recognition. We high- light the kernel view of orientation histograms, and show that they are equivalent to a certain type of match kernels over image patches. This novel view allows us to design a family of kernel descriptors which provide a unified and princi- pled framework to turn pixel attributes (gradient, color, local binary pattern, etc.) into compact patch-level features. In particular, we introduce three types of match kernels to measure similarities between image patches, and construct compact low-dimensional kernel descriptors from these match kernels using kernel princi- pal component analysis (KPCA) [23]. Kernel descriptors are easy to design and can turn any type of pixel attribute into patch-level features. They outperform carefully tuned and sophisticated features including SIFT and deep belief net- works. We report superior performance on standard image classification bench- marks: Scene-15, Caltech-101, CIFAR10 and CIFAR10-ImageNet. 1 Introduction Image representation (features) is arguably the most fundamental task in computer vision. The problem is highly challenging because images exhibit high variations, are highly structured, and lie in high dimensional spaces. In the past ten years, a large number of low-level features over images have been proposed. -
An FPGA-Accelerated Embedded Convolutional Neural Network
Master Thesis Report ZynqNet: An FPGA-Accelerated Embedded Convolutional Neural Network (edit) (edit) 1000ch 1000ch FPGA 1000ch Network Analysis Network Analysis 2x512 > 1024 2x512 > 1024 David Gschwend [email protected] SqueezeNet v1.1 b2a ext7 conv10 2x416 > SqueezeNet SqueezeNet v1.1 b2a ext7 conv10 2x416 > SqueezeNet arXiv:2005.06892v1 [cs.CV] 14 May 2020 Supervisors: Emanuel Schmid Felix Eberli Professor: Prof. Dr. Anton Gunzinger August 2016, ETH Zürich, Department of Information Technology and Electrical Engineering Abstract Image Understanding is becoming a vital feature in ever more applications ranging from medical diagnostics to autonomous vehicles. Many applications demand for embedded solutions that integrate into existing systems with tight real-time and power constraints. Convolutional Neural Networks (CNNs) presently achieve record-breaking accuracies in all image understanding benchmarks, but have a very high computational complexity. Embedded CNNs thus call for small and efficient, yet very powerful computing platforms. This master thesis explores the potential of FPGA-based CNN acceleration and demonstrates a fully functional proof-of-concept CNN implementation on a Zynq System-on-Chip. The ZynqNet Embedded CNN is designed for image classification on ImageNet and consists of ZynqNet CNN, an optimized and customized CNN topology, and the ZynqNet FPGA Accelerator, an FPGA-based architecture for its evaluation. ZynqNet CNN is a highly efficient CNN topology. Detailed analysis and optimization of prior topologies using the custom-designed Netscope CNN Analyzer have enabled a CNN with 84.5 % top-5 accuracy at a computational complexity of only 530 million multiply- accumulate operations. The topology is highly regular and consists exclusively of convolu- tional layers, ReLU nonlinearities and one global pooling layer.