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 301–325 of 1356 papers

TitleStatusHype
Is Modularity Transferable? A Case Study through the Lens of Knowledge DistillationCode0
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMsCode0
I3D: Transformer architectures with input-dependent dynamic depth for speech recognitionCode0
A Corrected Expected Improvement Acquisition Function Under Noisy ObservationsCode0
Image Classification with CondenseNeXt for ARM-Based Computing PlatformsCode0
Comb, Prune, Distill: Towards Unified Pruning for Vision Model CompressionCode0
MiniDisc: Minimal Distillation Schedule for Language Model CompressionCode0
Hybrid Binary Networks: Optimizing for Accuracy, Efficiency and MemoryCode0
Data-Free Backbone Fine-Tuning for Pruned Neural NetworksCode0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
Data-Free Adversarial DistillationCode0
HTR-JAND: Handwritten Text Recognition with Joint Attention Network and Knowledge DistillationCode0
GSB: Group Superposition Binarization for Vision Transformer with Limited Training SamplesCode0
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer LearningCode0
Gradual Channel Pruning while Training using Feature Relevance Scores for Convolutional Neural NetworksCode0
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student ArchitecturesCode0
Bayesian Optimization with Clustering and Rollback for CNN Auto PruningCode0
A Tunable Robust Pruning Framework Through Dynamic Network Rewiring of DNNsCode0
Cross-lingual Distillation for Text ClassificationCode0
From Dense to Sparse: Contrastive Pruning for Better Pre-trained Language Model CompressionCode0
Attribution-guided Pruning for Compression, Circuit Discovery, and Targeted Correction in LLMsCode0
Foundations of Large Language Model Compression -- Part 1: Weight QuantizationCode0
GASL: Guided Attention for Sparsity Learning in Deep Neural NetworksCode0
Data-free Knowledge Distillation for Fine-grained Visual CategorizationCode0
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model CompressionCode0
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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