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 151–175 of 1356 papers

TitleStatusHype
DQ-BART: Efficient Sequence-to-Sequence Model via Joint Distillation and QuantizationCode1
DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and TransformersCode1
Dynamic Channel Pruning: Feature Boosting and SuppressionCode1
Dynamic DNNs and Runtime Management for Efficient Inference on Mobile/Embedded DevicesCode1
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
Efficient Deep Learning: A Survey on Making Deep Learning Models Smaller, Faster, and BetterCode1
Efficient and Robust Quantization-aware Training via Adaptive Coreset SelectionCode1
Compacting, Picking and Growing for Unforgetting Continual LearningCode1
Clustered Sampling: Low-Variance and Improved Representativity for Clients Selection in Federated LearningCode1
Class Attention Transfer Based Knowledge DistillationCode1
Model LEGO: Creating Models Like Disassembling and Assembling Building BlocksCode1
Examining Post-Training Quantization for Mixture-of-Experts: A BenchmarkCode1
Fast Vocabulary Transfer for Language Model CompressionCode1
FAT: Learning Low-Bitwidth Parametric Representation via Frequency-Aware TransformationCode1
FFNeRV: Flow-Guided Frame-Wise Neural Representations for VideosCode1
FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix ApproximationCode1
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's DistanceCode1
Basic Binary Convolution Unit for Binarized Image Restoration NetworkCode1
Generative Model-based Feature Knowledge Distillation for Action RecognitionCode1
Global Sparse Momentum SGD for Pruning Very Deep Neural NetworksCode1
Activation-Informed Merging of Large Language ModelsCode1
A Winning Hand: Compressing Deep Networks Can Improve Out-Of-Distribution RobustnessCode1
Head Network Distillation: Splitting Distilled Deep Neural Networks for Resource-Constrained Edge Computing SystemsCode1
Backdoor Attacks on Federated Learning with Lottery Ticket HypothesisCode1
CHEX: CHannel EXploration for CNN Model CompressionCode1
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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