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Fast Hardware-Aware Neural Architecture Search

Fast Hardware-Aware Neural Architecture Search

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Li Lyna Zhang Yuqing Yang Yuhang Jiang Wenwu Zhu Yunxin Liu Microsoft Research Tsinghua University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Designing accurate and efficient convolutional neural architectures for vast amount of hardware is challenging because hardware designs are complex and diverse. This paper addresses the hardware diversity challenge in Neural Architecture Search (NAS). Unlike previous approaches that apply search algorithms on a small, human-designed search space without considering hardware diversity, we propose HURRICANE that explores the automatic hardware-aware search over a much larger search space and a two-stage search algorithm, to efficiently generate tailored models for different types of hardware. Extensive experiments on ImageNet demonstrate that our algorithm outperforms state-of-the-art hardware-aware NAS methods under the same latency constraint on three types of hardware. Moreover, the discovered architectures achieve much lower latency and higher accuracy than current state-of-the-art efficient models. Remarkably, HURRICANE achieves a 76.67% top-1 accuracy on ImageNet with a inference latency of only 16.5 ms for DSP, which is a 3.47% higher accuracy and a 6.35× inference speedup than FBNet-iPhoneX, respectively. For VPU, we achieve a 0.53% higher top-1 accuracy than Proxyless-mobile with a 1.49× speedup. Even for well-studied mobile CPU, we achieve a 1.63% higher top-1 accuracy than FBNet-iPhoneX with a comparable inference latency. HURRICANE also reduces the training time by 30.4% compared to SPOS.

关键词： Hardware computer architecture Graphics processing units Hurricanes Training Measurement

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The emotionally intelligent robot: Improving socially-aware human prediction in crowded environments 32

The emotionally intelligent robot: Improving socially-aware ...

引用

32nd ieee/cvf conference on computer vision and pattern recognition workshops, cvprw 2019

作者： Bera, Aniket Randhavane, Tanmay Manocha, Dinesh Department of Computer Science University of North Carolina Chapel Hill United States Department of Computer Science University of Maryland at College Park United States

ISBN: (纸本)9781728125060

— We present a real-time algorithm for emotion-aware navigation of a robot among pedestrians. Our approach estimates time-varying emotional behaviors of pedestrians from their faces and trajectories using a combination of Bayesian-inference, CNN-based learning, and the PAD (Pleasure-Arousal-Dominance) model from psychology. These PAD characteristics are used for long-term path prediction and generating proxemic constraints for each pedestrian. We use a multi-channel model to classify pedestrian characteristics into four emotion categories (happy, sad, angry, neutral). In our validation results, we observe an emotion detection accuracy of 85.33%. We formulate emotion-based proxemic constraints to perform socially-aware robot navigation in low- to medium-density environments. We demonstrate the benefits of our algorithm in simulated environments with tens of pedestrians as well as in a real-world setting with Pepper, a social humanoid robot. © 2019 ieee computer Society. All rights reserved.

关键词： Anthropomorphic robots

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Analyzing U-Net Robustness for Single Cell Nucleus Segmentation from Phase Contrast Images

Analyzing U-Net Robustness for Single Cell Nucleus Segmentat...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Chenyi Ling Michael Halter Anne Plant Michael Majurski Jeffrey Stinson Joe Chalfoun Software and Systems Division Information Technology Lab NIST Gaithersburg MD Biosystems and Biomaterials Division Material Measurement Lab NIST Gaithersburg MD

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

We quantify the robustness of the semantic segmentation model U-Net, applied to single cell nuclei detection, with respect to the following factors: (1) automated vs manual training annotations, (2) quantity of training data, and (3) microscope image focus. The difficulty of obtaining sufficient volumes of accurate manually annotated training data to create an accurate Convolutional Neural Networks (CNN) model is overcome by the temporary use of fluorescent labels to automate the creation of training datasets using traditional image processing algorithms. The accuracy measurement is computed with respect to manually annotated masks which were also created to evaluate the effectiveness of using automated training set generation via the fluorescent images. The metric to compute the accuracy is the false positive/negative rate of cell nuclei detection. The goal is to maximize the true positive rate while minimizing the false positive rate. We found that automated segmentation of fluorescently labeled nuclei provides viable training data without the need for manual segmentation. A training dataset size of four large stitched images with medium cell density was enough to reach a true positive rate above 88% and a false positive rate below 20%.

关键词： Image segmentation Training computer architecture Microprocessors Microscopy Manuals

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OC-FakeDect: Classifying Deepfakes Using One-class Variational Autoencoder

OC-FakeDect: Classifying Deepfakes Using One-class Variation...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hasam Khalid Simon S. Woo Computer Science and Engineering Department Sungkyunkwan University South Korea

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

An image forgery method called Deepfakes can cause security and privacy issues by changing the identity of a person in a photo through the replacement of his/her face with a computer-generated image or another person's face. Therefore, a new challenge of detecting Deepfakes arises to protect individuals from potential misuses. Many researchers have proposed various binary-classification based detection approaches to detect deepfakes. However, binary-classification based methods generally require a large amount of both real and fake face images for training, and it is challenging to collect sufficient fake images data in advance. Besides, when new deepfakes generation methods are introduced, little deepfakes data will be available, and the detection performance may be mediocre. To overcome these data scarcity limitations, we formulate deepfakes detection as a one-class anomaly detection problem. We propose OC-FakeDect, which uses a one-class Variational Autoencoder (VAE) to train only on real face images and detects non-real images such as deepfakes by treating them as anomalies. Our preliminary result shows that our one class-based approach can be promising when detecting Deepfakes, achieving a 97.5% accuracy on the NeuralTextures data of the well-known FaceForensics++ benchmark dataset without using any fake images for the training process.

关键词： Face Training Image reconstruction Forensics Streaming media Anomaly detection Benchmark testing

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A topological encoding convolutional neural network for segmentation of 3D multiphoton images of brain vasculature using persistent homology

A topological encoding convolutional neural network for segm...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Mohammad Haft-Javaherian Martin Villiger Chris B. Schaffer Nozomi Nishimura Polina Golland Brett E. Bouma Wellman Center for Photomedicine Massachusetts General Hospital Harvard Medical School Boston MA USA Meinig School of Biomedical Engineering Cornell University Ithaca NY USA Computer Science and Artificial Intelligence Laboratory (CSAIL) Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

The clinical evidence suggests that cognitive disorders are associated with vasculature dysfunction and decreased blood flow in the brain. Hence, a functional understanding of the linkage between brain functionality and the vascular network is essential. However, methods to systematically and quantitatively describe and compare structures as complex as brain blood vessels are lacking. 3D imaging modalities such as multiphoton microscopy enables researchers to capture the network of brain vasculature with high spatial resolutions. Nonetheless, image processing and inference are some of the bottlenecks for biomedical research involving imaging, and any advancement in this area impacts many research groups. Here, we propose a topological encoding convolutional neural network based on persistent homology to segment 3D multiphoton images of brain vasculature. We demonstrate that our model outperforms state-of-the-art models in terms of the Dice coefficient and it is comparable in terms of other metrics such as sensitivity. Additionally, the topological characteristics of our model's segmentation results mimic manual ground truth. Our code and model are open source at https://***/mhaft/DeepVess.

关键词： Image segmentation Three-dimensional displays Biological system modeling computer vision computer architecture Biomedical imaging

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TherISuRNet - A Computationally Efficient Thermal Image Super-Resolution Network

TherISuRNet - A Computationally Efficient Thermal Image Supe...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Vishal Chudasama Heena Patel Kalpesh Prajapati Kishor Upla Raghavendra Ramachandra Kiran Raja Christoph Busch Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat India Norwegian University of Science and Technology (NTNU) Gjøvik Norway

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Human perception is limited to perceive the objects beyond the range of visible wavelengths in the Electromagnetic (EM) spectrum. This prevents them to recognize objects in different conditions such as poor illumination or severe weather (e.g., under fog or smoke). The technological advancement in thermographic imaging enables the visualization of objects beyond visible range which enables it's use in many applications such as military, medical, agriculture, etc. However, due to the hardware constraints, the thermal cameras are limited with poor spatial resolution when compared to similar visible range RGB cameras. In this paper, we propose a Super-Resolution (SR) of thermal images using a deep neural network architecture which we refer to as TherISuRNet. We use progressive upscaling strategy with asymmetrical residual learning in the network which is computationally efficient for different upscaling factors such as ×2, ×3 and ×4. The proposed architecture consists of different modules for low and high-frequency feature extraction along with upsampling blocks. The effectiveness of the proposed architecture in TherISuRNet is verified by evaluating it with different datasets. The obtained results indicate superior results as compared to other state-of-the-art SR methods.

关键词： Cameras Spatial resolution Feature extraction Training computer architecture

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When Person Re-identification Meets Changing Clothes

When Person Re-identification Meets Changing Clothes

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Fangbin Wan Yang Wu Xuelin Qian Yixiong Chen Yanwei Fu School of Data Science Fudan University Kyoto University School of Computer Science Fudan University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Person re-identification (ReID) is now an active research topic for AI-based video surveillance applications such as specific person search, but the practical issue that the target person(s) may change clothes (clothes inconsistency problem) has been overlooked for long. For the first time, this paper systematically studies this problem. We first overcome the difficulty of lack of suitable dataset, by collecting a small yet representative real dataset for testing whilst building a large realistic synthetic dataset for training and deeper studies. Facilitated by our new datasets, we are able to conduct various interesting new experiments for studying the influence of clothes inconsistency. We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes. Representative existing ReID models are adopted to show informative results on such a challenging setting, and we also provide some preliminary efforts on improving the robustness of existing models on handling the clothes inconsistency issue in the data. We believe that this study can be inspiring and helpful for encouraging more researches in this direction. The dataset is available on the project website: https://***/***.

关键词： Cameras Benchmark testing Lighting Buildings Games Data models Biological system modeling

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AMC-Loss: Angular Margin Contrastive Loss for Improved Explainability in Image Classification

AMC-Loss: Angular Margin Contrastive Loss for Improved Expla...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Hongjun Choi Anirudh Som Pavan Turaga Geometric Media Lab School of Arts Media and Engineering Arizona State University

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Deep-learning architectures for classification problems involve the cross-entropy loss sometimes assisted with auxiliary loss functions like center loss, contrastive loss and triplet loss. These auxiliary loss functions facilitate better discrimination between the different classes of interest. However, recent studies hint at the fact that these loss functions do not take into account the intrinsic angular distribution exhibited by the low-level and high-level feature representations. This results in less compactness between samples from the same class and unclear boundary separations between data clusters of different classes. In this paper, we address this issue by proposing the use of geometric constraints, rooted in Riemannian geometry. Specifically, we propose Angular Margin Contrastive Loss (AMC-Loss), a new loss function to be used along with the traditional cross-entropy loss. The AMC-Loss employs the discriminative angular distance metric that is equivalent to geodesic distance on a hypersphere manifold such that it can serve a clear geometric interpretation. We demonstrate the effectiveness of AMC-Loss by providing quantitative and qualitative results. We find that although the proposed geometrically constrained loss-function improves quantitative results modestly, it has a qualitatively surprisingly beneficial effect on increasing the interpretability of deep-net decisions as seen by the visual explanations generated by techniques such as the Grad-CAM. Our code is available at https://***/hchoi71/AMC-Loss.

关键词： Training Task analysis Feature extraction Euclidean distance Airplanes computer vision Media

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Ternary MobileNets via Per-Layer Hybrid Filter Banks

Ternary MobileNets via Per-Layer Hybrid Filter Banks

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Dibakar Gope Jesse Beu Urmish Thakker Matthew Mattina Arm ML Research Lab

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

MobileNets family of computer vision neural networks have fueled tremendous progress in the design and organization of resource-efficient architectures in recent years. New applications with stringent real-time requirements on highly constrained devices require further compression of MobileNets-like compute-efficient networks. Model quantization is a widely used technique to compress and accelerate neural network inference and prior works have quantized MobileNets to 4 - 6 bits, albeit with a modest to significant drop in accuracy. While quantization to sub-byte values (i.e. precision ≤ 8 bits) has been valuable, even further quantization of MobileNets to binary or ternary values is necessary to realize significant energy savings and possibly runtime speedups on specialized hardware, such as ASICs and FPGAs. Under the key observation that convolutional filters at each layer of a deep neural network may respond differently to ternary quantization, we propose a novel quantization method that generates per-layer hybrid filter banks consisting of full-precision and ternary weight filters for MobileNets. Using this proposed quantization method, we quantize a substantial portion of weight filters of MobileNets to ternary values resulting in a 27.98% savings in energy, and a 51.07% reduction in the model size, while achieving comparable accuracy and no degradation in throughput on specialized hardware in comparison to the baseline full-precision MobileNets. Finally, we demonstrate the generalizability and effectiveness of hybrid filter banks to other neural network architectures.

关键词： Convolution Quantization (signal) computer architecture Neural networks Throughput Hardware Computational modeling

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Systematic Evaluation of Backdoor Data Poisoning Attacks on Image Classifiers

Systematic Evaluation of Backdoor Data Poisoning Attacks on ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Loc Truong Chace Jones Brian Hutchinson Andrew August Brenda Praggastis Robert Jasper Nicole Nichols Aaron Tuor Western Washington University Pacific Northwest National Laboratory

ISBN: (数字)9781728193601

ISBN: (纸本)9781728193618

Backdoor data poisoning attacks have recently been demonstrated in computer vision research as a potential safety risk for machine learning (ML) systems. Traditional data poisoning attacks manipulate training data to induce unreliability of an ML model, whereas backdoor data poisoning attacks maintain system performance unless the ML model is presented with an input containing an embedded "trigger " that provides a predetermined response advantageous to the adversary. Our work builds upon prior backdoor data-poisoning research for ML image classifiers and systematically assesses different experimental conditions including types of trigger patterns, persistence of trigger patterns during retraining, poisoning strategies, architectures (ResNet-50, NasNet, NasNet-Mobile), datasets (Flowers, CIFAR-10), and potential defensive regularization techniques (Contrastive Loss, Logit Squeezing, Manifold Mixup, Soft-Nearest-Neighbors Loss). Experiments yield four key findings. First, the success rate of backdoor poisoning attacks varies widely, depending on several factors, including model architecture, trigger pattern and regularization technique. Second, we find that poisoned models are hard to detect through performance inspection alone. Third, regularization typically reduces backdoor success rate, although it can have no effect or even slightly increase it, depending on the form of regularization. Finally, backdoors inserted through data poisoning can be rendered ineffective after just a few epochs of additional training on a small set of clean data without affecting the model's performance.

关键词： Data models Training Computational modeling Machine learning Training data computer vision Safety

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