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 751–775 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
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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