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A Simple Approach and Benchmark for 21,000-Category Object Detection
Knowledge Condensation Distillation
Reducing Information Loss for Spiking Neural Networks
Masked Generative Distillation
Fine-Grained Data Distribution Alignment for Post-Training Quantization
Learning with Recoverable Forgetting
Efficient One Pass Self-Distillation with Zipfs Label Smoothing
Prune Your Model before Distill It
Deep Partial Updating: Towards Communication Efficient Updating for On-Device Inference
Patch Similarity Aware Data-Free Quantization for Vision Transformers
L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training
Streaming Multiscale Deep Equilibrium Models
Symmetry Regularization and Saturating Nonlinearity for Robust Quantization
SP-Net: Slowly Progressing Dynamic Inference Networks
Equivariance and Invariance Inductive Bias for Learning from Insufficient Data
Mixed-Precision Neural Network Quantization via Learned Layer-Wise Importance
Event Neural Networks
EdgeViTs: Competing Light-Weight CNNs on Mobile Devices with Vision Transformers
PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators
Disentangled Differentiable Network Pruning
IDa-Det: An Information Discrepancy-Aware Distillation for 1-Bit Detectors
Learning to Weight Samples for Dynamic Early-Exiting Networks
AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets
Adaptive Token Sampling for Efficient Vision Transformers
Weight Fixing Networks
Self-Slimmed Vision Transformer
Switchable Online Knowledge Distillation
-Robustness and Beyond: Unleashing Efficient Adversarial Training
Multi-Granularity Pruning for Model Acceleration on Mobile Devices
Deep Ensemble Learning by Diverse Knowledge Distillation for Fine-Grained Object Classification
Helpful or Harmful: Inter-Task Association in Continual Learning
Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies
SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks
Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning
Network Binarization via Contrastive Learning
Lipschitz Continuity Retained Binary Neural Network
SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning
Soft Masking for Cost-Constrained Channel Pruning
Non-uniform Step Size Quantization for Accurate Post-Training Quantization
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning
Meta-GF: Training Dynamic-Depth Neural Networks Harmoniously
Towards Ultra Low Latency Spiking Neural Networks for Vision and Sequential Tasks Using Temporal Pruning
Towards Accurate Network Quantization with Equivalent Smooth Regularizer.
Knowledge Condensation Distillation
Reducing Information Loss for Spiking Neural Networks
Masked Generative Distillation
Fine-Grained Data Distribution Alignment for Post-Training Quantization
Learning with Recoverable Forgetting
Efficient One Pass Self-Distillation with Zipfs Label Smoothing
Prune Your Model before Distill It
Deep Partial Updating: Towards Communication Efficient Updating for On-Device Inference
Patch Similarity Aware Data-Free Quantization for Vision Transformers
L3: Accelerator-Friendly Lossless Image Format for High-Resolution, High-Throughput DNN Training
Streaming Multiscale Deep Equilibrium Models
Symmetry Regularization and Saturating Nonlinearity for Robust Quantization
SP-Net: Slowly Progressing Dynamic Inference Networks
Equivariance and Invariance Inductive Bias for Learning from Insufficient Data
Mixed-Precision Neural Network Quantization via Learned Layer-Wise Importance
Event Neural Networks
EdgeViTs: Competing Light-Weight CNNs on Mobile Devices with Vision Transformers
PalQuant: Accelerating High-Precision Networks on Low-Precision Accelerators
Disentangled Differentiable Network Pruning
IDa-Det: An Information Discrepancy-Aware Distillation for 1-Bit Detectors
Learning to Weight Samples for Dynamic Early-Exiting Networks
AdaBin: Improving Binary Neural Networks with Adaptive Binary Sets
Adaptive Token Sampling for Efficient Vision Transformers
Weight Fixing Networks
Self-Slimmed Vision Transformer
Switchable Online Knowledge Distillation
-Robustness and Beyond: Unleashing Efficient Adversarial Training
Multi-Granularity Pruning for Model Acceleration on Mobile Devices
Deep Ensemble Learning by Diverse Knowledge Distillation for Fine-Grained Object Classification
Helpful or Harmful: Inter-Task Association in Continual Learning
Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies
SPIN: An Empirical Evaluation on Sharing Parameters of Isotropic Networks
Ensemble Knowledge Guided Sub-network Search and Fine-Tuning for Filter Pruning
Network Binarization via Contrastive Learning
Lipschitz Continuity Retained Binary Neural Network
SPViT: Enabling Faster Vision Transformers via Latency-Aware Soft Token Pruning
Soft Masking for Cost-Constrained Channel Pruning
Non-uniform Step Size Quantization for Accurate Post-Training Quantization
SuperTickets: Drawing Task-Agnostic Lottery Tickets from Supernets via Jointly Architecture Searching and Parameter Pruning
Meta-GF: Training Dynamic-Depth Neural Networks Harmoniously
Towards Ultra Low Latency Spiking Neural Networks for Vision and Sequential Tasks Using Temporal Pruning
Towards Accurate Network Quantization with Equivalent Smooth Regularizer.