Article Digests

• Pure Alogrithm
  • Convolutional Networks with Adaptive Inference Graphs
  • Dynamic Resolution Network
  • Dynamic Neural Networks: A Survey
  • Reducing overfitting in deep networks by decorrelating representations
  • Regularizing cnns with locally constrained decorrelations
  • U-Net: Convolutional Networks for Biomedical Image Segmentation
  • Semi-Supervised Classification with Graph Convolutional Networks
• CV
  • 3D
    • 3D Classification
      • PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
    • 3D Detection
      • PointPillars: Fast Encoders for Object Detection from Point Clouds
  • BackBone
    • An image is worth 16x16 words: Transformers for image recognition at scale
  • Image Detection
    • End-to-End Object Detection with Transformers
  • LLCV
    • Image Denoise
      • Transfer Learning from Synthetic to Real-Noise Denoising with Adaptive Instance Normalization
      • Brief review of image denoising techniques
      • Toward Convolutional Blind Denoising of Real Photographs
      • Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency
      • Benchmarking Denoising Algorithms with Real Photographs
      • Deep Learning for Image Denoising： A Survey
      • Deep Learning on Image Denoising An overview
      • Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
      • Dynamic Residual Dense Network for Image Denoising
      • Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
      • FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
      • Image Blind Denoising With Generative Adversarial Network Based Noise Modeling
      • HINet： Half Instance Normalization Network for Image Restoration
      • Learning Deep CNN Denoiser Prior for Image Restoration
      • Learning Raw Image Denoising with Bayer Pattern Unification and Bayer Preserving Augmentation
      • Neural Nearest Neighbors Network
      • Practical Deep Raw Image Denoising on Mobile Devices
      • Generalized Deep Image to Image Regression
      • Real Image Denoising with Feature Attention
      • Spatial-Adaptive Network for Single Image Denoising
      • A High-Quality Denoising Dataset for Smartphone Cameras
      • Robust Image Denoising with Texture-Aware Neural Network
      • Unprocessing Images for Learned Raw Denoising
      • Noise2Noise: Learning Image Restoration without Clean Data
    • Low Light Enhancement
      • Restoring Extremely Dark Images in Real Time
      • Learning to See in the Dark
    • Restoration
      • CLEARER: Multi-Scale Neural Architecture Search for Image Restoration
      • CycleISP: Real Image Restoration via Improved Data Synthesis
      • Deep Image Prior
      • Learning Enriched Features for Real Image Restoration and Enhancement
      • Multi-Stage Progressive Image Restoration
      • MemNet: A Persistent Memory Network for Image Restoration
      • Self-Guided Network for Fast Image Denoising
      • Stacking Networks Dynamically for Image Restoration Based on the Plug-and-Play Framework
      • Memory-Efficient Hierarchical Neural Architecture Search for Image Denoising
      • Attentive Fine-Grained Structured Sparsity for Image Restoration
      • Restormer: Efficient Transformer for High-Resolution Image Restoration
      • Uformer: A General U-Shaped Transformer for Image Restoration
      • Searching for Controllable Image Restoration Networks
      • Enhanced Image Restoration Via Supervised Target Feature Transfer
    • Super Resolution
      • A Layer-Wise Extreme Network Compression for Super Resolution
      • Aligned Structured Sparsity Learning for Efficient Image Super-Resolution
      • Binarized Neural Network for Single Image Super Resolution
      • Fully !antized Image Super-Resolution Networks
      • PAMS: Quantized Super-Resolution via Parameterized Max Scale
      • Deep Learning for Image Super-resolution: A Survey
      • Training Binary Neural Network without Batch Normalization for Image Super-Resolution
      • Video super‑resolution based on deep learning: a comprehensive survey
      • BasicVSR: The Search for Essential Components in Video Super-Resolution and Beyond
      • Enhanced Deep Residual Networks for Single Image Super-Resolution
      • Extremely Lightweight Quantization Robust Real-Time Single-Image Super Resolution for Mobile Devices
      • CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
      • Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution
      • Dynamic Dual Trainable Bounds for Ultra-low Precision Super-Resolution Networks
      • Adaptive Patch Exiting for Scalable Single Image Super-Resolution
      • Achieving on-Mobile Real-Time Super-Resolution with Neural Architecture and Pruning Search
      • Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning
      • Fine-grained neural architecture search for image super-resolution
      • Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
      • DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks
      • Wide Activation for Efficient and Accurate Image Super-Resolution
  • Uncategorized
    • ABPN: Adaptive Blend Pyramid Network for Real-Time Local Retouching of Ultra High-Resolution Photo
    • Lite Pose: Efficient Architecture Design for 2D Human Pose Estimation
• Computer Architecture
  • A Survey of Computer Architecture Simulation Techniques and Tools
  • DNNAbacus: Toward Accurate Computational Cost Prediction for Deep Neural Networks
  • MAPLE-Edge: A Runtime Latency Predictor for Edge Devices
  • STONNE: Enabling Cycle-Level Microarchitectural Simulation for DNN Inference Accelerators
  • An End-To-End Toolchain: From Automated Cost Modeling to Static WCET and WCEC Analysis
  • MLPerf Mobile Inference Benchmark
  • torch. fx: Practical program capture and transformation for deep learning in python
  • nn-Meter: Towards Accurate Latency Prediction of Deep-Learning Model Inference on Diverse Edge Devices
  • EcoFlow Efficient Convolutional Dataflows on Low-Power Neural Network Accelerators
• Model Compression
  • GhostNet: More Features from Cheap Operations
  • MCUNet: Tiny Deep Learning on IoT Devices
  • MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learning
  • TinyTL: Reduce Activations, Not Trainable Parameters for Efficient On-Device Learning
  • 深度神经网络压缩与加速综述
  • BNN related articles
    • Towards Accurate Binary Convolutional Neural Network
    • BATS: Binary ArchitecTure Search
    • Bayesian Optimized 1-Bit CNNs
    • Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm
    • DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
    • Learning Frequency Domain Approximation for Binary Neural Networks
    • High-Capacity Expert Binary Networks
    • Learning Channel-wise Interactions for Binary Convolutional Neural Networks
    • ReCU: Reviving the Dead Weights in Binary Neural Networks
    • Training Binary Neural Networks with Real-to-Binary Convlutions
    • Training Binary Neural Networks through Learning with Noisy Supervision
    • Understanding Straight-Through Estimator in Training Activation Quantized Neural Nets
    • WRPN: Wide Reduced-Precision Networks
    • XNOR-Net
    • PokeBNN: A Binary Pursuit of Lightweight Accuracy
    • ReActNet Towards Precise Binary Neural Network with Generalized Activation Functions
    • Bitwise Neural Networks
    • BiT: Robustly Binarized Multi-distilled Transformer
    • BinaryDuo: Reducing Gradient Mismatch in Binary Activation Network by Coupling Binary Activations
    • BoolNet: Minimizing the Energy Consumption of Binary Neural Networks
    • An Empirical study of Binary Neural Networks' Optimisation
  • Deployment
    • Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency
    • Fast Camera Image Denoising on Mobile GPUs with Deep Learning
  • Kownledge Distillation
    • A Comprehensive Overhaul of Feature Distillation
    • Improve object detection with feature-based knowledge distillation: Towards accurate and efficient detectors
  • ML System
    • A systematic methodology for analysis of deep learning hardware and software platforms
    • Precious: Resource-Demand Estimation for Embedded Neural Network Accelerators
    • Learned TPU Cost Model for XLA Tensor Programs
  • NAS
    • Neural Architecture Search for Dense Prediction Tasks in Computer Vision
    • A Generic Graph-Based Neural Architecture Encoding Scheme for Predictor-Based NAS
    • Neural Predictor for Neural Architecture Search
    • NAS-BENCH-201: Extending the Scope of Reproducible Neural Architecture Search
    • A Generic Graph-based Neural Architecture Encoding Scheme with Multifaceted Information
  • Pruning
    • Architecture-Aware Network Pruning for Vision Quality Applications
    • Structured Pruning of Neural Networks with Budget-Aware Regularization
    • ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model
    • Revisiting Random Channel Pruning for Neural Network Compression
    • DHP: Differentiable Meta Pruning via HyperNetworks
    • Universally Slimmable Networks and Improved Training Techniques
    • SLIMMABLE NEURAL NETWORKS
    • Learning N: M Fine-grained Structured Sparse Neural Networks From Scratch
    • AutoSlim: Towards One-Shot Architecture Search for Channel Numbers
  • Quantization
    • A Survey of Quantization Methods for Efficient Neural Network Inference
    • Post training 4-bit quantization of convolutional networks for rapid-deployment
    • Improving Post Training Neural Quantization: Layer-wise Calibration and Integer Programming
    • Up or Down? Adaptive Rounding for Post-Training Quantization
    • Automated Log-Scale Quantization for Low-Cost Deep Neural Networks
    • BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction
    • Data-Free Quantization Through Weight Equalization and Bias Correction
    • Differentiable Soft Quantization: Bridging Full-Precision and Low-Bit Neural Networks
    • Deep Learning with Limited Numerical Precision
    • Loss Aware Post-training Quantization
    • Learnable Companding Quantization for Accurate Low-bit Neural Networks
    • LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
    • Learned Step Size Quantization
    • MQBench: Towards Reproducible and Deployable Model Quantization Benchmark
    • Improving Neural Network Quantization without Retraining using Outlier Channel Splitting
    • Trained quantization thresholds for accurate and efficient fixed-point inference of deep neural networks
    • ZeroQ: A Novel Zero Shot Quantization Framework
    • NoisyQuant: Noisy Bias-Enhanced Post-Training Activation Quantization for Vision Transformers
    • PACT: Parameterized Clipping Activation for Quantized Neural Networks
    • Quantization Applications
      • Post-training Quantization on Diffusion Models
      • SmoothQuant: Accurate and Efficient Post-Training Quantization for Large Language Models
      • LLM-QAT: Data-Free Quantization Aware Training for Large Language Models
      • Quantizable Transformers Removing Outliers by Helping Attention Heads Do Nothing
      • Q-DM: An Efficient Low-bit Quantized Diffusion Model
• Unassorted
  • EsyMo: Scalable and Efficient Deep-Learning Inference on Asymmetric Mobile CPUs
  • Elf: Accelerate High-resolution Mobile Deep Vision with Content-aware Parallel Offloading
  • Flexible High-resolution Object Detection on Edge Devices with Tunable Latency
  • CoDL: Efficient CPU-GPU Co-execution for Deep Learning Inference on Mobile Devices
  • Melon: Breaking the Memory Wall for Resource-Efficient On-Device Machine Learning
  • Automatic heterogeneous quantization of deep neural networks for low-latency inference on the edge for particle detectors
  • Optical Flow Estimation using a Spatial Pyramid Network
  • Scaling Up Your Kernels to 31x31: Revisiting Large Kernel Design in CNNs