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 626–650 of 1356 papers

TitleStatusHype
Model Compression Methods for YOLOv5: A Review—0
Impact of Disentanglement on Pruning Neural Networks—0
Knowledge Distillation for Object Detection: from generic to remote sensing datasets—0
CA-LoRA: Adapting Existing LoRA for Compressed LLMs to Enable Efficient Multi-Tasking on Personal DevicesCode0
Distilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series DataCode0
Distilled Pruning: Using Synthetic Data to Win the LotteryCode0
TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—0
An Efficient Sparse Inference Software Accelerator for Transformer-based Language Models on CPUs—0
Low-Rank Prune-And-Factorize for Language Model Compression—0
Feature Adversarial Distillation for Point Cloud Classification—0
Partitioning-Guided K-Means: Extreme Empty Cluster Resolution for Extreme Model Compression—0
Data-Free Backbone Fine-Tuning for Pruned Neural NetworksCode0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
LoSparse: Structured Compression of Large Language Models based on Low-Rank and Sparse Approximation—0
Neural Network Compression using Binarization and Few Full-Precision Weights—0
Deep Model Compression Also Helps Models Capture AmbiguityCode0
A Brief Review of Hypernetworks in Deep LearningCode0
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation—0
Modular Transformers: Compressing Transformers into Modularized Layers for Flexible Efficient Inference—0
Low-Complexity Acoustic Scene Classification Using Data Augmentation and Lightweight ResNet—0
Group channel pruning and spatial attention distilling for object detection—0
Task-Agnostic Structured Pruning of Speech Representation Models—0
ConaCLIP: Exploring Distillation of Fully-Connected Knowledge Interaction Graph for Lightweight Text-Image Retrieval—0
2-bit Conformer quantization for automatic speech recognition—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