Paper

• Model compression as constrained optimization, with application to neural nets. Part I: general framework
• Model compression as constrained optimization, with application to neural nets. Part II: quantization
• A Survey of Model Compression and Acceleration for Deep Neural Networks
• Dynamic Capacity Networks
• ResNeXt: Aggregated Residual Transformations for Deep Neural Networks
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Xception: Deep Learning with Depthwise Separable Convolutions
• ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
• ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression
• SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size
• Residual Attention Network for Image Classification
• SEP-Nets: Small and Effective Pattern Networks
• Deep Networks with Stochastic Depth
• Learning Infinite Layer Networks Without the Kernel Trick
• Coordinating Filters for Faster Deep Neural Networks
• ResBinNet: Residual Binary Neural Network
• Squeezedet: Unified, small, low power fully convolutional neural networks
• Efficient Sparse-Winograd Convolutional Neural Networks
• DSD: Dense-Sparse-Dense Training for Deep Neural Networks
• Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
• Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation
• Dark knowledge
• FitNets: Hints for Thin Deep Nets
• Distilling the Knowledge in a Neural Network
• MobileID: Face Model Compression by Distilling Knowledge from Neurons
• DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
• Deep Model Compression: Distilling Knowledge from Noisy Teachers
• Paying More Attention to Attention: Improving the Performance of Convolutional Neural Networks via Attention Transfer
• Sequence-Level Knowledge Distillation
• Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
• Learning Efficient Object Detection Models with Knowledge Distillation
• Data-Free Knowledge Distillation For Deep Neural Networks
• Learning Loss for Knowledge Distillation with Conditional Adversarial Networks
• Knowledge Projection for Effective Design of Thinner and Faster Deep Neural Networks
• Moonshine: Distilling with Cheap Convolutions
• Model Distillation with Knowledge Transfer from Face Classification to Alignment and Verification
• Local Binary Convolutional Neural Networks
• Binarized Convolutional Neural Networks with Separable Filters for Efficient Hardware Acceleration
• Binarized Neural Networks: Training Deep Neural Networks with Weights and Activations Constrained to +1 or -1
• XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks
• DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients
• Quantize weights and activations in Recurrent Neural Networks
• The ZipML Framework for Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning
• Quantized Convolutional Neural Networks for Mobile Devices
• Compressing Deep Convolutional Networks using Vector Quantization
• Quantized Neural Networks: Training Neural Networks with Low Precision Weights and Activations
• Fixed-Point Performance Analysis of Recurrent Neural Networks
• Loss-aware Binarization of Deep Networks
• Towards the Limit of Network Quantization
• Deep Learning with Low Precision by Half-wave Gaussian Quantization
• ShiftCNN: Generalized Low-Precision Architecture for Inference of Convolutional Neural Networks
• Trained Ternary Quantization
• Data-Driven Sparse Structure Selection for Deep Neural Networks
• Fine-Pruning: Joint Fine-Tuning and Compression of a Convolutional Network with Bayesian Optimization
• Learning to Prune: Exploring the Frontier of Fast and Accurate Parsing
• Designing Energy-Efficient Convolutional Neural Networks using Energy-Aware Pruning
• Pruning Filters for Efficient ConvNets
• Pruning Convolutional Neural Networks for Resource Efficient Inference
• Soft Weight-Sharing for Neural Network Compression
• Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding
• Learning both Weights and Connections for Efficient Neural Networks
• Dynamic Network Surgery for Efficient DNNs
• ESE: Efficient Speech Recognition Engine with Sparse LSTM on FPGA
• Faster CNNs with Direct Sparse Convolutions and Guided Pruning
• Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
• Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications
• Efficient and Accurate Approximations of Nonlinear Convolutional Networks
• Accelerating Very Deep Convolutional Networks for Classification and Detection
• Convolutional neural networks with low-rank regularization
• Speeding up convolutional neural networks with low rank expansions
• Compressing Deep Convolutional Networks using Vector Quantization
• Inverted Residuals and Linear Bottlenecks: Mobile Networks for Classification, Detection and Segmentation
• Squeezedet: Unified, small, low power fully convolutional neural networks
• A Survey of Model Compression and Acceleration for Deep Neural Networks
• Dynamic Capacity Networks
• ResNeXt: Aggregated Residual Transformations for Deep Neural Networks
• MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
• Xception: Deep Learning with Depthwise Separable Convolutions
• Residual Attention Network for Image Classification
• SEP-Nets: Small and Effective Pattern Networks
• Deep Networks with Stochastic Depth
• Learning Infinite Layer Networks Without the Kernel Trick
• Coordinating Filters for Faster Deep Neural Networks
• Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
• Efficient and Accurate Approximations of Nonlinear Convolutional Networks