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 51–75 of 1356 papers

TitleStatusHype
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
Compression Laws for Large Language Models—0
Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model CompressionCode0
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation—0
Compositionality Unlocks Deep Interpretable Models—0
Random Conditioning with Distillation for Data-Efficient Diffusion Model Compression—0
Penrose Tiled Low-Rank Compression and Section-Wise Q&A Fine-Tuning: A General Framework for Domain-Specific Large Language Model Adaptation—0
Multi-Task Semantic Communications via Large Models—0
Boosting Large Language Models with Mask Fine-TuningCode0
Q-MambaIR: Accurate Quantized Mamba for Efficient Image Restoration—0
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness—0
Delving Deep into Semantic Relation Distillation—0
A Low-Power Streaming Speech Enhancement Accelerator For Edge Devices—0
Large Language Model Compression via the Nested Activation-Aware Decomposition—0
Temporal Action Detection Model Compression by Progressive Block Drop—0
InhibiDistilbert: Knowledge Distillation for a ReLU and Addition-based Transformer—0
CompMarkGS: Robust Watermarking for Compressed 3D Gaussian Splatting—0
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning—0
Fragile Mastery: Are Domain-Specific Trade-Offs Undermining On-Device Language Models?—0
SVD-LLM V2: Optimizing Singular Value Truncation for Large Language Model CompressionCode3
Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge—0
Position-Aware Depth Decay Decoding (D^3): Boosting Large Language Model Inference Efficiency—0
Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices—0
Towards Superior Quantization Accuracy: A Layer-sensitive Approach—0
ACAM-KD: Adaptive and Cooperative Attention Masking for Knowledge Distillation—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