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 476–500 of 1356 papers

TitleStatusHype
Densely Distilling Cumulative Knowledge for Continual Learning—0
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting—0
Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models—0
NurtureNet: A Multi-task Video-based Approach for Newborn Anthropometry—0
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks—0
Light Field Compression Based on Implicit Neural Representation—0
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth—0
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision TransformerCode0
Iterative Filter Pruning for Concatenation-based CNN ArchitecturesCode0
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models—0
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation—0
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization—0
Data-free Knowledge Distillation for Fine-grained Visual CategorizationCode0
Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
Structured Model Pruning for Efficient Inference in Computational Pathology—0
Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing—0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
Multilingual Brain Surgeon: Large Language Models Can be Compressed Leaving No Language BehindCode0
Improve Knowledge Distillation via Label Revision and Data Selection—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
Automated Inference of Graph Transformation Rules—0
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL—0
Enhancing Inference Efficiency of Large Language Models: Investigating Optimization Strategies and Architectural Innovations—0
Instance-Aware Group Quantization for Vision Transformers—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