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 251–275 of 1356 papers

TitleStatusHype
Aerial Image Classification in Scarce and Unconstrained Environments via Conformal Prediction—0
From Large to Super-Tiny: End-to-End Optimization for Cost-Efficient LLMs—0
D^2MoE: Dual Routing and Dynamic Scheduling for Efficient On-Device MoE-based LLM Serving—0
ImPart: Importance-Aware Delta-Sparsification for Improved Model Compression and Merging in LLMsCode0
Efficient Hybrid Language Model Compression through Group-Aware SSM Pruning—0
APSQ: Additive Partial Sum Quantization with Algorithm-Hardware Co-DesignCode0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
Thanos: A Block-wise Pruning Algorithm for Efficient Large Language Model CompressionCode0
Compression Laws for Large Language Models—0
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
Multi-Task Semantic Communications via Large Models—0
Penrose Tiled Low-Rank Compression and Section-Wise Q&A Fine-Tuning: A General Framework for Domain-Specific Large Language Model Adaptation—0
Delving Deep into Semantic Relation Distillation—0
Boosting Large Language Models with Mask Fine-TuningCode0
MoQa: Rethinking MoE Quantization with Multi-stage Data-model Distribution Awareness—0
A Low-Power Streaming Speech Enhancement Accelerator For Edge Devices—0
Q-MambaIR: Accurate Quantized Mamba for Efficient Image Restoration—0
Temporal Action Detection Model Compression by Progressive Block Drop—0
Large Language Model Compression via the Nested Activation-Aware Decomposition—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
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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