Real-time Federated Evolutionary Neural Architecture Search (Zhu and Jin. 2020) https://arxiv.org/abs/2003.02793

BATS: Binary ArchitecTure Search (Bulat et al. 2020) https://arxiv.org/abs/2003.01711

ADWPNAS: Architecture-Driven Weight Prediction for Neural Architecture Search (Zhang et al. 2020) https://arxiv.org/abs/2003.01335

NAS-Count: Counting-by-Density with Neural Architecture Search (Hu et al. 2020) https://arxiv.org/abs/2003.00217

ImmuNetNAS: An Immune-network approach for searching Convolutional Neural Network Architectures (Kefan and Pang. 2020) https://arxiv.org/abs/2002.12704

Neural Inheritance Relation Guided One-Shot Layer Assignment Search (Meng et al. 2020) https://arxiv.org/abs/2002.12580

Automatically Searching for U-Net Image Translator Architecture (Shu and Wang. 2020) https://arxiv.org/abs/2002.11581

AutoEmb: Automated Embedding Dimensionality Search in Streaming Recommendations (Zhao et al. 2020) https://arxiv.org/abs/2002.11252

Memory-Efficient Models for Scene Text Recognition via Neural Architecture Search (Hong et al. 2020; accepted at WACV’20 workshop) http://openaccess.thecvf.com/content_WACVW_2020/papers/w3/Hong_Memory-Efficient_Models_for_Scene_Text_Recognition_via_Neural_Architecture_Search_WACVW_2020_paper.pdf

Search for Winograd-Aware Quantized Networks (Fernandez-Marques et al. 2020) https://arxiv.org/abs/2002.10711

Semi-Supervised Neural Architecture Search (Luo et al. 2020) https://arxiv.org/abs/2002.10389

Neural Architecture Search for Compressed Sensing Magnetic Resonance Image Reconstruction (Yan et al. 2020) https://arxiv.org/abs/2002.09625

DSNAS: Direct Neural Architecture Search without Parameter Retraining (Hu et al. 2020) https://arxiv.org/abs/2002.09128

Neural Architecture Search For Fault Diagnosis (Li et al. 2020; accepted at ESREL’20) https://arxiv.org/abs/2002.07997

Learning Architectures for Binary Networks (Singh et al. 2020) https://arxiv.org/pdf/2002.06963.pdf

Efficient Evolutionary Architecture Search for CNN Optimization on GTSRB (Johner and Wassner. 2020; accepted at ICMLA’19) https://ieeexplore.ieee.org/abstract/document/8999305/

Automating Deep Neural Network Model Selection for Edge Inference (Lu et al. 2020; accepted at CogMI’20) https://ieeexplore.ieee.org/abstract/document/8998995

Neural Architecture Search over Decentralized Data (Xu et al. 2020) https://arxiv.org/abs/2002.06352

Automatic Structural Search for Multi-task Learning VALPs (Garciarena et al. 2020; accepted at OLA’20) https://link.springer.com/chapter/10.1007/978-3-030-41913-4_3

RandomNet: Towards Fully Automatic Neural Architecture Design for Multimodal Learning (Alletto et al. 2020; accepted at Meta-Eval 2020 workshop) http://eval.how/aaai-2020/REAIS19_p9.pdf

Classifying the classifier: dissecting the weight space of neural networks (Eilertsen et al. 2020) https://arxiv.org/pdf/2002.05688.pdf

Stabilizing Differentiable Architecture Search via Perturbation-based Regularization (Chen and Hsieh. 2020) https://arxiv.org/abs/2002.05283

Best of Both Worlds: AutoML Codesign of a CNN and its Hardware Accelerator (Abdelfattah et al. 2020; accepted at DAC’20) https://arxiv.org/abs/2002.05022

Variational Depth Search in ResNets (Antoran et al. 2020) https://arxiv.org/abs/2002.02797

Co-Exploration of Neural Architectures and Heterogeneous ASIC Accelerator Designs Targeting Multiple Tasks (Yang et al. 2020; accepted at DAC’20) https://arxiv.org/abs/2002.04116

FPNet: Customized Convolutional Neural Network for FPGA Platforms (Yang et al. 2020; accepted at FPT’20) https://ieeexplore.ieee.org/abstract/document/8977837

AutoFCL: Automatically Tuning Fully Connected Layers for Transfer Learning (Basha et al. 2020) https://arxiv.org/abs/2001.11951

NASS: Optimizing Secure Inference via Neural Architecture Search (Bian et al. 2020; accepted at ECAI’20) https://arxiv.org/abs/2001.11854

Search for Better Students to Learn Distilled Knowledge (Gu et al. 2020) https://arxiv.org/abs/2001.11612

Bayesian Neural Architecture Search using A Training-Free Performance Metric (Camero et al. 2020) https://arxiv.org/abs/2001.10726

NAS-Bench-1Shot1: Benchmarking and Dissecting One-Short Neural Architecture Search (Zela et al. 2020; accepted at ICLR’20) https://arxiv.org/abs/2001.10422

Convolution Neural Network Architecture Learning for Remote Sensing Scene Classification (Chen et al. 2010) https://arxiv.org/abs/2001.09614

Multi-objective Neural Architecture Search via Non-stationary Policy Gradient (Chen et al. 2020) https://arxiv.org/abs/2001.08437

Efficient Neural Architecture Search: A Broad Version (Ding et al. 2020) https://arxiv.org/abs/2001.06679

ENAS U-Net: Evolutionary Neural Architecture Search for Retinal Vessel (Fan et al. 2020) https://arxiv.org/abs/2001.06678

FlexiBO: Cost-Aware Multi-Objective Optimization of Deep Neural Networks (Iqbal et al. 2020) https://arxiv.org/abs/2001.06588

Up to two billion times acceleration of scientific simulations with deep neural architecture search (Kasim et al. 2020) https://arxiv.org/abs/2001.08055

Latency-Aware Differentiable Neural Architecture Search (Xu et al. 2020) https://arxiv.org/abs/2001.06392

MixPath: A Unified Approach for One-shot Neural Architecture Search (Chu et al. 2020) https://arxiv.org/abs/2001.05887

Neural Architecture Search for Skin Lesion Classification (Kwasigroch et al. 2020; accepted at IEEE Access) https://ieeexplore.ieee.org/document/8950333

AdaBERT: Task-Adaptive BERT Compression with Differentiable Neural Architecture Search (Chen et al. 2020) https://arxiv.org/abs/2001.04246

Neural Architecture Search for Deep Image Prior (Ho et al. 2020) https://arxiv.org/abs/2001.04776

Fast Neural Network Adaptation via Parameter Remapping and Architecture Search (Fang et al. 2020; accepted at ICLR’20) https://arxiv.org/abs/2001.02525

FTT-NAS: Discovering Fault-Tolerant Neural Architecture (Li et al. 2020; accepted at ASP-DAC 2020) http://nicsefc.ee.tsinghua.edu.cn/media/publications/2020/ASPDAC20_293_6p4Ghq4.pdf

Deeper Insights into Weight Sharing in Neural Architecture Search (Zhang et al. 2020) https://arxiv.org/abs/2001.01431

EcoNAS: Finding Proxies for Economical Neural Architecture Search (Zhou et al. 2020; accepted at CVPR’20) https://arxiv.org/abs/2001.01233

DeepMaker: A multi-objective optimization framework for deep neural networks in embedded systems (Loni et al. 2020; accepted at Microprocessors and Microsystems) https://www.sciencedirect.com/science/article/abs/pii/S0141933119301176

Auto-ORVNet: Orientation-boosted Volumetric Neural Architecture Search for 3D Shape Classification (Ma et al. 2020; accepted at IEEE Access) https://ieeexplore.ieee.org/abstract/document/8939365

NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search (Dong and Yang et al. 2020; accepted at ICLR’20) https://arxiv.org/abs/2001.00326

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相关内容

CVPR2020-Code

CVPR 2020 论文开源项目合集,同时欢迎各位大佬提交issue,分享CVPR 2020开源项目

图像分类

Spatially Attentive Output Layer for Image Classification

目标检测

Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection

BiDet: An Efficient Binarized Object Detector

3D目标检测

Point-GNN: Graph Neural Network for 3D Object Detection in a Point Cloud

目标跟踪

MAST: A Memory-Augmented Self-supervised Tracker

语义分割

Cars Can't Fly up in the Sky: Improving Urban-Scene Segmentation via Height-driven Attention Networks

实例分割

PolarMask: Single Shot Instance Segmentation with Polar Representation

CenterMask : Real-Time Anchor-Free Instance Segmentation

Deep Snake for Real-Time Instance Segmentation

视频目标分割

State-Aware Tracker for Real-Time Video Object Segmentation

Learning Fast and Robust Target Models for Video Object Segmentation

NAS

Rethinking Performance Estimation in Neural Architecture Search

CARS: Continuous Evolution for Efficient Neural Architecture Search

GAN

Watch your Up-Convolution: CNN Based Generative Deep Neural Networks are Failing to Reproduce Spectral Distributions

Re-ID

Weakly supervised discriminative feature learning with state information for person identification

3D点云

点云卷积

FPConv: Learning Local Flattening for Point Convolution

3D点云配准

D3Feat: Joint Learning of Dense Detection and Description of 3D Local Features

人脸检测

活体检测

Searching Central Difference Convolutional Networks for Face Anti-Spoofing

人脸表情识别

Suppressing Uncertainties for Large-Scale Facial Expression Recognition

人体姿态估计

2D人体姿态估计

The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation

Distribution-Aware Coordinate Representation for Human Pose Estimation

3D人体姿态估计

Compressed Volumetric Heatmaps for Multi-Person 3D Pose Estimation

VIBE: Video Inference for Human Body Pose and Shape Estimation

Back to the Future: Joint Aware Temporal Deep Learning 3D Human Pose Estimation

Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS

点云

点云分类

PointAugment: an Auto-Augmentation Framework for Point Cloud Classification

场景文本检测

ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

场景文本识别

ABCNet: Real-time Scene Text Spotting with Adaptive Bezier-Curve Network

超分辨率

视频超分辨率

Zooming Slow-Mo: Fast and Accurate One-Stage Space-Time Video Super-Resolution

模型剪枝

HRank: Filter Pruning using High-Rank Feature Map

行为识别

人群计数

深度估计

单目深度估计

Domain Decluttering: Simplifying Images to Mitigate Synthetic-Real Domain Shift and Improve Depth Estimation

去模糊

视频去模糊

Cascaded Deep Video Deblurring Using Temporal Sharpness Prior

视觉问答

视觉问答

VC R-CNN:Visual Commonsense R-CNN

视觉语言导航

Towards Learning a Generic Agent for Vision-and-Language Navigation via Pre-training

视频压缩

Learning for Video Compression with Hierarchical Quality and Recurrent Enhancement

行人轨迹预测

Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

数据集

IntrA: 3D Intracranial Aneurysm Dataset for Deep Learning

Cross-View Tracking for Multi-Human 3D Pose Estimation at over 100 FPS

其他

GhostNet: More Features from Cheap Operations

AdderNet: Do We Really Need Multiplications in Deep Learning?

Deep Image Harmonization via Domain Verification

Blurry Video Frame Interpolation

Extremely Dense Point Correspondences using a Learned Feature Descriptor

Filter Grafting for Deep Neural Networks

Action Segmentation with Joint Self-Supervised Temporal Domain Adaptation

Detecting Attended Visual Targets in Video

Deep Image Spatial Transformation for Person Image Generation

Rethinking Zero-shot Video Classification: End-to-end Training for Realistic Applications

https://github.com/charlesCXK/3D-SketchAware-SSC

https://github.com/Anonymous20192020/Anonymous_CVPR5767

https://github.com/avirambh/ScopeFlow

https://github.com/csbhr/CDVD-TSP

https://github.com/ymcidence/TBH

https://github.com/yaoyao-liu/mnemonics

https://github.com/meder411/Tangent-Images

https://github.com/KaihuaTang/Scene-Graph-Benchmark.pytorch

https://github.com/sjmoran/deep_local_parametric_filters

https://github.com/charlesCXK/3D-SketchAware-SSC

https://github.com/bermanmaxim/AOWS

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In this paper, we propose a residual non-local attention network for high-quality image restoration. Without considering the uneven distribution of information in the corrupted images, previous methods are restricted by local convolutional operation and equal treatment of spatial- and channel-wise features. To address this issue, we design local and non-local attention blocks to extract features that capture the long-range dependencies between pixels and pay more attention to the challenging parts. Specifically, we design trunk branch and (non-)local mask branch in each (non-)local attention block. The trunk branch is used to extract hierarchical features. Local and non-local mask branches aim to adaptively rescale these hierarchical features with mixed attentions. The local mask branch concentrates on more local structures with convolutional operations, while non-local attention considers more about long-range dependencies in the whole feature map. Furthermore, we propose residual local and non-local attention learning to train the very deep network, which further enhance the representation ability of the network. Our proposed method can be generalized for various image restoration applications, such as image denoising, demosaicing, compression artifacts reduction, and super-resolution. Experiments demonstrate that our method obtains comparable or better results compared with recently leading methods quantitatively and visually.

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This paper addresses the scalability challenge of architecture search by formulating the task in a differentiable manner. Unlike conventional approaches of applying evolution or reinforcement learning over a discrete and non-differentiable search space, our method is based on the continuous relaxation of the architecture representation, allowing efficient search of the architecture using gradient descent. Extensive experiments on CIFAR-10, ImageNet, Penn Treebank and WikiText-2 show that our algorithm excels in discovering high-performance convolutional architectures for image classification and recurrent architectures for language modeling, while being orders of magnitude faster than state-of-the-art non-differentiable techniques.

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