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 376–400 of 1356 papers

TitleStatusHype
COP: Customized Deep Model Compression via Regularized Correlation-Based Filter-Level PruningCode0
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural NetworksCode0
Asymmetric Masked Distillation for Pre-Training Small Foundation ModelsCode0
GASL: Guided Attention for Sparsity Learning in Deep Neural NetworksCode0
FLoCoRA: Federated learning compression with low-rank adaptationCode0
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model CompressionCode0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student ArchitecturesCode0
Distilling Focal Knowledge From Imperfect Expert for 3D Object DetectionCode0
Binary Classification as a Phase Separation ProcessCode0
How does topology of neural architectures impact gradient propagation and model performance?Code0
Knowledge Distillation as Semiparametric InferenceCode0
Lottery Aware Sparsity Hunting: Enabling Federated Learning on Resource-Limited EdgeCode0
Few Shot Network Compression via Cross DistillationCode0
Computer Vision Model Compression Techniques for Embedded Systems: A SurveyCode0
Faithful Label-free Knowledge DistillationCode0
Finding Deviated Behaviors of the Compressed DNN Models for Image ClassificationsCode0
Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model CompressionCode0
Exploiting Kernel Sparsity and Entropy for Interpretable CNN CompressionCode0
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence GuaranteeCode0
Adversarial Robustness vs. Model Compression, or Both?Code0
Exact Backpropagation in Binary Weighted Networks with Group Weight TransformationsCode0
Exploring Gradient Flow Based Saliency for DNN Model CompressionCode0
Enhancing Knowledge Distillation of Large Language Models through Efficient Multi-Modal Distribution AlignmentCode0
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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