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 1101–1125 of 1356 papers

TitleStatusHype
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
Two-Step Knowledge Distillation for Tiny Speech Enhancement—0
Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs—0
When Compression Meets Model Compression: Memory-Efficient Double Compression for Large Language Models—0
UDC: Unified DNAS for Compressible TinyML Models—0
MobiSR: Efficient On-Device Super-Resolution through Heterogeneous Mobile Processors—0
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG—0
Adaptive Quantization of Neural Networks—0
Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models—0
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams—0
Scaling Laws for Deep Learning—0
SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners—0
SDQ: Sparse Decomposed Quantization for LLM Inference—0
Search for Better Students to Learn Distilled Knowledge—0
Adaptive Neural Connections for Sparsity Learning—0
3U-EdgeAI: Ultra-Low Memory Training, Ultra-Low BitwidthQuantization, and Ultra-Low Latency Acceleration—0
SeKron: A Decomposition Method Supporting Many Factorization Structures—0
Understanding and Improving Knowledge Distillation—0
Selective Convolutional Units: Improving CNNs via Channel Selectivity—0
XAI-BayesHAR: A novel Framework for Human Activity Recognition with Integrated Uncertainty and Shapely Values—0
Self-calibration for Language Model Quantization and Pruning—0
Understanding LLMs: A Comprehensive Overview from Training to Inference—0
Self-Supervised Generative Adversarial Compression—0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
YANMTT: Yet Another Neural Machine Translation Toolkit—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