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 351–375 of 1356 papers

TitleStatusHype
VQ4ALL: Efficient Neural Network Representation via a Universal Codebook—0
Compression for Better: A General and Stable Lossless Compression Framework—0
Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search—0
Efficient Model Compression Techniques with FishLeg—0
CPTQuant -- A Novel Mixed Precision Post-Training Quantization Techniques for Large Language Models—0
Individual Content and Motion Dynamics Preserved Pruning for Video Diffusion Models—0
Faithful Label-free Knowledge DistillationCode0
Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion—0
TaQ-DiT: Time-aware Quantization for Diffusion Transformers—0
FASTNav: Fine-tuned Adaptive Small-language-models Trained for Multi-point Robot Navigation—0
What Makes a Good Dataset for Knowledge Distillation?—0
Puppet-CNN: Input-Adaptive Convolutional Neural Networks with Model Compression using Ordinary Differential Equation—0
Bridging the Resource Gap: Deploying Advanced Imitation Learning Models onto Affordable Embedded Platforms—0
An exploration of the effect of quantisation on energy consumption and inference time of StarCoder2Code0
Re-Parameterization of Lightweight Transformer for On-Device Speech Emotion Recognition—0
Feature Interaction Fusion Self-Distillation Network For CTR Prediction—0
ASER: Activation Smoothing and Error Reconstruction for Large Language Model Quantization—0
OWLed: Outlier-weighed Layerwise Pruning for Efficient Autonomous Driving FrameworkCode0
Optimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning—0
From Word Vectors to Multimodal Embeddings: Techniques, Applications, and Future Directions For Large Language Models—0
Change Is the Only Constant: Dynamic LLM Slicing based on Layer RedundancyCode0
Efficient Model Compression for Bayesian Neural Networks—0
ML Research BenchmarkCode0
EoRA: Training-free Compensation for Compressed LLM with Eigenspace Low-Rank Approximation—0
SWITCH: Studying with Teacher for Knowledge Distillation of Large 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