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

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Showing 601–650 of 1356 papers

TitleStatusHype
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''—0
VIC-KD: Variance-Invariance-Covariance Knowledge Distillation to Make Keyword Spotting More Robust Against Adversarial Attacks—0
Pruning Large Language Models via Accuracy Predictor—0
Training dynamic models using early exits for automatic speech recognition on resource-constrained devicesCode0
Two-Step Knowledge Distillation for Tiny Speech Enhancement—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
Training Acceleration of Low-Rank Decomposed Networks using Sequential Freezing and Rank Quantization—0
Norm Tweaking: High-performance Low-bit Quantization of Large Language Models—0
Compressing Vision Transformers for Low-Resource Visual LearningCode0
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
Uncovering the Hidden Cost of Model CompressionCode0
Computation-efficient Deep Learning for Computer Vision: A Survey—0
Improving Knowledge Distillation for BERT Models: Loss Functions, Mapping Methods, and Weight Tuning—0
DLIP: Distilling Language-Image Pre-training—0
QD-BEV : Quantization-aware View-guided Distillation for Multi-view 3D Object Detection—0
Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking—0
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems—0
Spike-and-slab shrinkage priors for structurally sparse Bayesian neural networks—0
Benchmarking Adversarial Robustness of Compressed Deep Learning Models—0
Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction—0
A Survey on Model Compression for Large Language Models—0
FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing—0
Resource Constrained Model Compression via Minimax Optimization for Spiking Neural NetworksCode0
Accurate Neural Network Pruning Requires Rethinking Sparse Optimization—0
MIMONet: Multi-Input Multi-Output On-Device Deep Learning—0
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