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 426–450 of 1356 papers

TitleStatusHype
Tensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs—0
NeuSemSlice: Towards Effective DNN Model Maintenance via Neuron-level Semantic Slicing—0
Comprehensive Study on Performance Evaluation and Optimization of Model Compression: Bridging Traditional Deep Learning and Large Language Models—0
Generalizing Teacher Networks for Effective Knowledge Distillation Across Student ArchitecturesCode0
LORTSAR: Low-Rank Transformer for Skeleton-based Action Recognition—0
Compressed models are NOT miniature versions of large models—0
Mamba-PTQ: Outlier Channels in Recurrent Large Language Models—0
Minimizing PLM-Based Few-Shot Intent DetectorsCode0
Inference Optimization of Foundation Models on AI Accelerators—0
Explicit-NeRF-QA: A Quality Assessment Database for Explicit NeRF Model CompressionCode0
Beyond Perplexity: Multi-dimensional Safety Evaluation of LLM CompressionCode0
Quantizing YOLOv7: A Comprehensive Study—0
The Impact of Quantization and Pruning on Deep Reinforcement Learning Models—0
AMD: Automatic Multi-step Distillation of Large-scale Vision Models—0
Efficient DNN-Powered Software with Fair Sparse Models—0
MLKD-BERT: Multi-level Knowledge Distillation for Pre-trained Language Models—0
FoldGPT: Simple and Effective Large Language Model Compression Scheme—0
MCNC: Manifold Constrained Network Compression—0
Speeding Up Image Classifiers with Little Companions—0
Exploring compressibility of transformer based text-to-music (TTM) models—0
Reinforced Knowledge Distillation for Time Series RegressionCode0
FLoCoRA: Federated learning compression with low-rank adaptationCode0
Failure-Resilient Distributed Inference with Model Compression over Heterogeneous Edge Devices—0
SDQ: Sparse Decomposed Quantization for LLM Inference—0
Finding Task-specific Subnetworks in Multi-task Spoken Language Understanding Model—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