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 576–600 of 1356 papers

TitleStatusHype
FedCode: Communication-Efficient Federated Learning via Transferring Codebooks—0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—0
What is Lost in Knowledge Distillation?—0
Supervised domain adaptation for building extraction from off-nadir aerial images—0
Asymmetric Masked Distillation for Pre-Training Small Foundation ModelsCode0
Data-Free Distillation of Language Model by Text-to-Text Transfer—0
Divergent Token Metrics: Measuring degradation to prune away LLM components -- and optimize quantization—0
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression—0
LXMERT Model Compression for Visual Question AnsweringCode0
Data-Free Knowledge Distillation Using Adversarially Perturbed OpenGL Shader Images—0
In defense of parameter sharing for model-compression—0
USDC: Unified Static and Dynamic Compression for Visual Transformer—0
Efficient Apple Maturity and Damage Assessment: A Lightweight Detection Model with GAN and Attention Mechanism—0
What do larger image classifiers memorise?—0
Accelerating Machine Learning Primitives on Commodity Hardware—0
A Corrected Expected Improvement Acquisition Function Under Noisy ObservationsCode0
Model Compression in Practice: Lessons Learned from Practitioners Creating On-device Machine Learning Experiences—0
Robustness-Guided Image Synthesis for Data-Free Quantization—0
Sparse Deep Learning for Time Series Data: Theory and Applications—0
ECoFLaP: Efficient Coarse-to-Fine Layer-Wise Pruning for Vision-Language Models—0
Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation—0
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning—0
Bridging the Gap Between Foundation Models and Heterogeneous Federated Learning—0
Distilling Inductive Bias: Knowledge Distillation Beyond Model Compression—0
CAIT: Triple-Win Compression towards High Accuracy, Fast Inference, and Favorable Transferability For ViTs—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