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 751–800 of 1356 papers

TitleStatusHype
Sometimes Painful but Certainly Promising: Feasibility and Trade-offs of Language Model Inference at the Edge—0
SpaLLM: Unified Compressive Adaptation of Large Language Models with Sketching—0
Sparse Deep Learning for Time Series Data: Theory and Applications—0
Sparse Unbalanced GAN Training with In-Time Over-Parameterization—0
Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks—0
Compressible Spectral Mixture Kernels with Sparse Dependency Structures for Gaussian Processes—0
Spectral Pruning: Compressing Deep Neural Networks via Spectral Analysis and its Generalization Error—0
Speeding up Convolutional Neural Networks with Low Rank Expansions—0
Speeding Up Image Classifiers with Little Companions—0
Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer—0
Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense via Model Pruning—0
SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency—0
Stability Based Filter Pruning for Accelerating Deep CNNs—0
Effective Model Compression via Stage-wise Pruning—0
Statistical Model Compression for Small-Footprint Natural Language Understanding—0
Strategic Fusion Optimizes Transformer Compression—0
Streamlining Tensor and Network Pruning in PyTorch—0
Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach—0
Structured Compression by Weight Encryption for Unstructured Pruning and Quantization—0
Structured Convolutions for Efficient Neural Network Design—0
Structured Model Pruning for Efficient Inference in Computational Pathology—0
Structured Multi-Hashing for Model Compression—0
Structured Pruning for Multi-Task Deep Neural Networks—0
Structured Pruning is All You Need for Pruning CNNs at Initialization—0
Structured Pruning Learns Compact and Accurate Models—0
SubCharacter Chinese-English Neural Machine Translation with Wubi encoding—0
Sub-network Multi-objective Evolutionary Algorithm for Filter Pruning—0
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning—0
Survey of Dropout Methods for Deep Neural Networks—0
Swallowing the Poison Pills: Insights from Vulnerability Disparity Among LLMs—0
SwapNet: Efficient Swapping for DNN Inference on Edge AI Devices Beyond the Memory Budget—0
Sweeping Heterogeneity with Smart MoPs: Mixture of Prompts for LLM Task Adaptation—0
Swing Distillation: A Privacy-Preserving Knowledge Distillation Framework—0
SWITCH: Studying with Teacher for Knowledge Distillation of Large Language Models—0
SWSC: Shared Weight for Similar Channel in LLM—0
Synergistic Effects of Knowledge Distillation and Structured Pruning for Self-Supervised Speech Models—0
Introducing Pose Consistency and Warp-Alignment for Self-Supervised 6D Object Pose Estimation in Color Images—0
TAID: Temporally Adaptive Interpolated Distillation for Efficient Knowledge Transfer in Language Models—0
TaQ-DiT: Time-aware Quantization for Diffusion Transformers—0
Task-Agnostic and Adaptive-Size BERT Compression—0
Task-Agnostic Structured Pruning of Speech Representation Models—0
Diffusion Model Compression for Image-to-Image Translation—0
Temporal Action Detection Model Compression by Progressive Block Drop—0
Tensor Contraction Layers for Parsimonious Deep Nets—0
TensorGPT: Efficient Compression of Large Language Models based on Tensor-Train Decomposition—0
Tensorial Neural Networks: Generalization of Neural Networks and Application to Model Compression—0
Tensorization is a powerful but underexplored tool for compression and interpretability of neural networks—0
Test-Time Adaptation Toward Personalized Speech Enhancement: Zero-Shot Learning with Knowledge Distillation—0
Tetra-AML: Automatic Machine Learning via Tensor Networks—0
TextPruner: A Model Pruning Toolkit for Pre-Trained Language Models—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