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 1–25 of 1356 papers

TitleStatusHype
LINR-PCGC: Lossless Implicit Neural Representations for Point Cloud Geometry Compression—0
DipSVD: Dual-importance Protected SVD for Efficient LLM Compression—0
RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models—0
Model compression using knowledge distillation with integrated gradients—0
Simple is what you need for efficient and accurate medical image segmentationCode0
Attribution-guided Pruning for Compression, Circuit Discovery, and Targeted Correction in LLMsCode0
EAQuant: Enhancing Post-Training Quantization for MoE Models via Expert-Aware OptimizationCode0
FIMA-Q: Post-Training Quantization for Vision Transformers by Fisher Information Matrix ApproximationCode1
Advances in Small-Footprint Keyword Spotting: A Comprehensive Review of Efficient Models and AlgorithmsCode0
Post-Training Quantization for Video Matting—0
AWP: Activation-Aware Weight Pruning and Quantization with Projected Gradient Descent—0
Structured Pruning and Quantization for Learned Image CompressionCode0
Smooth Model Compression without Fine-Tuning—0
INSIGHT: A Survey of In-Network Systems for Intelligent, High-Efficiency AI and Topology Optimization—0
FLAT-LLM: Fine-grained Low-rank Activation Space Transformation for Large Language Model CompressionCode0
Effective and Efficient One-pass Compression of Speech Foundation Models Using Sparsity-aware Self-pinching Gates—0
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks—0
ResSVD: Residual Compensated SVD for Large Language Model Compression—0
Pangu Light: Weight Re-Initialization for Pruning and Accelerating LLMs—0
Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation—0
Efficient Speech Translation through Model Compression and Knowledge DistillationCode0
Making deep neural networks work for medical audio: representation, compression and domain adaptation—0
Efficient and Workload-Aware LLM Serving via Runtime Layer Swapping and KV Cache Resizing—0
Knowledge Grafting of Large Language ModelsCode0
LatentLLM: Attention-Aware Joint Tensor Compression—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