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 551–575 of 1356 papers

TitleStatusHype
Explainability-Driven Leaf Disease Classification Using Adversarial Training and Knowledge Distillation—0
DMT: Comprehensive Distillation with Multiple Self-supervised Teachers—0
Integrating Fairness and Model Pruning Through Bi-level Optimization—0
Unraveling Key Factors of Knowledge Distillation—0
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment—0
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models—0
Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroupCode0
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
Understanding the Effect of Model Compression on Social Bias in Large Language ModelsCode0
Language Model Knowledge Distillation for Efficient Question Answering in SpanishCode0
The Efficiency Spectrum of Large Language Models: An Algorithmic SurveyCode0
Physics Inspired Criterion for Pruning-Quantization Joint LearningCode0
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated LearningCode0
Towards Higher Ranks via Adversarial Weight Pruning—0
LayerCollapse: Adaptive compression of neural networks—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
Cosine Similarity Knowledge Distillation for Individual Class Information Transfer—0
Education distillation:getting student models to learn in shcools—0
Knowledge Distillation Based Semantic Communications For Multiple Users—0
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper—0
Efficient Transformer Knowledge Distillation: A Performance Review—0
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs—0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review—0
On the Impact of Calibration Data in Post-training Quantization and Pruning—0
A Speed Odyssey for Deployable Quantization of LLMs—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