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 201–225 of 1356 papers

TitleStatusHype
PromptKD: Distilling Student-Friendly Knowledge for Generative Language Models via Prompt TuningCode1
Designing Large Foundation Models for Efficient Training and Inference: A SurveyCode1
Deep Compression for PyTorch Model Deployment on MicrocontrollersCode1
PSAQ-ViT V2: Towards Accurate and General Data-Free Quantization for Vision TransformersCode1
An Empirical Study of CLIP for Text-based Person SearchCode1
DS-Net++: Dynamic Weight Slicing for Efficient Inference in CNNs and TransformersCode1
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural NetworksCode1
Retraining-free Model Quantization via One-Shot Weight-Coupling LearningCode1
Neural Pruning via Growing RegularizationCode1
Artemis: HE-Aware Training for Efficient Privacy-Preserving Machine Learning—0
Are We There Yet? A Measurement Study of Efficiency for LLM Applications on Mobile Devices—0
Accelerating Very Deep Convolutional Networks for Classification and Detection—0
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques—0
Accelerating Machine Learning Primitives on Commodity Hardware—0
Compressing Recurrent Neural Networks for FPGA-accelerated Implementation in Fluorescence Lifetime Imaging—0
Architecture Compression—0
A Progressive Sub-Network Searching Framework for Dynamic Inference—0
A Deep Cascade Network for Unaligned Face Attribute Classification—0
Compressing Pre-trained Language Models by Matrix Decomposition—0
Compressing Recurrent Neural Networks Using Hierarchical Tucker Tensor Decomposition—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework—0
2-bit Conformer quantization for automatic speech recognition—0
Approximability and Generalisation—0
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy—0
Show:102550
← PrevPage 9 of 55Next →
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