SOTAVerified

Model Compression

Model Compression is an actively pursued area of research over the last few years with the goal of deploying state-of-the-art deep networks in low-power and resource limited devices without significant drop in accuracy. Parameter pruning, low-rank factorization and weight quantization are some of the proposed methods to compress the size of deep networks.

Source: KD-MRI: A knowledge distillation framework for image reconstruction and image restoration in MRI workflow

Papers

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 851–875 of 1356 papers

TitleStatusHype
Data-Free Knowledge Distillation for Image Super-ResolutionCode0
Quantized Neural Networks via -1, +1 Encoding Decomposition and AccelerationCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and BetterCode1
Topology Distillation for Recommender System—0
Masked Training of Neural Networks with Partial Gradients—0
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution RobustnessCode1
Energy-efficient Knowledge Distillation for Spiking Neural Networks—0
Heterogeneous Federated Learning using Dynamic Model Pruning and Adaptive Gradient—0
ModelDiff: Testing-Based DNN Similarity Comparison for Model Reuse DetectionCode1
FedNILM: Applying Federated Learning to NILM Applications at the Edge—0
FedNL: Making Newton-Type Methods Applicable to Federated Learning—0
Feature Flow Regularization: Improving Structured Sparsity in Deep Neural Networks—0
Bidirectional Distillation for Top-K Recommender SystemCode1
You Only Compress Once: Towards Effective and Elastic BERT Compression via Exploit-Explore Stochastic Nature GradientCode1
Enabling Lightweight Fine-tuning for Pre-trained Language Model Compression based on Matrix Product OperatorsCode1
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers—0
Energy-Efficient Model Compression and Splitting for Collaborative Inference Over Time-Varying Channels—0
On Attention Redundancy: A Comprehensive Study—0
NAS-BERT: Task-Agnostic and Adaptive-Size BERT Compression with Neural Architecture Search—0
Towards Efficient Full 8-bit Integer DNN Online Training on Resource-limited Devices without Batch Normalization—0
Super Tickets in Pre-Trained Language Models: From Model Compression to Improving GeneralizationCode1
Differentiable Sparsification for Deep Neural Networks—0
Model Compression—0
How to Explain Neural Networks: an Approximation Perspective—0
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All datasetsImageNetQNLI

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ADLIK-MO-ResNet50+W4A4Top-177.88—Unverified
2ADLIK-MO-ResNet50+W3A4Top-177.34—Unverified
3ResNet-18 + 4bit-1dim model compression using DKMTop-170.52—Unverified
4MobileNet-v1 + 4bit-1dim model compression using DKMTop-169.63—Unverified
5ResNet-18 + 2bit-1dim model compression using DKMTop-168.63—Unverified
6MobileNet-v1 + 2bit-1dim model compression using DKMTop-167.62—Unverified
7ResNet-18 + 4bit-4dim model compression using DKMTop-166.1—Unverified
8ResNet-18 + 2bit-2dim model compression using DKMTop-164.7—Unverified
9MobileNet-v1 + 4bit-4dim model compression using DKMTop-161.4—Unverified
10ResNet-18 + 1bit-1dim model compression using DKMTop-159.7—Unverified
#ModelMetricClaimedVerifiedStatus
1MobileBERT + 2bit-1dim model compression using DKMAccuracy82.13—Unverified
2MobileBERT + 1bit-1dim model compression using DKMAccuracy63.17—Unverified