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 451–475 of 1356 papers

TitleStatusHype
Architecture Compression—0
Compacting Deep Neural Networks for Internet of Things: Methods and Applications—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
Compact CNN Structure Learning by Knowledge Distillation—0
A Progressive Sub-Network Searching Framework for Dynamic Inference—0
A Deep Cascade Network for Unaligned Face Attribute Classification—0
Accelerating Machine Learning Primitives on Commodity Hardware—0
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth—0
Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Models—0
ELRT: Efficient Low-Rank Training for Compact Convolutional Neural Networks—0
Communication-Efficient Distributed Online Learning with Kernels—0
A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework—0
E-LANG: Energy-Based Joint Inferencing of Super and Swift Language Models—0
Efficient Transformer Knowledge Distillation: A Performance Review—0
Approximability and Generalisation—0
Efficient Supernet Training with Orthogonal Softmax for Scalable ASR Model Compression—0
Efficient Speech Representation Learning with Low-Bit Quantization—0
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy—0
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization—0
Accelerating Linear Recurrent Neural Networks for the Edge with Unstructured Sparsity—0
Efficient Pruning of Text-to-Image Models: Insights from Pruning Stable Diffusion—0
Efficient Point Cloud Classification via Offline Distillation Framework and Negative-Weight Self-Distillation Technique—0
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—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