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 1026–1050 of 1356 papers

TitleStatusHype
Cascaded channel pruning using hierarchical self-distillation—0
Towards Modality Transferable Visual Information Representation with Optimal Model Compression—0
Adaptive Learning of Tensor Network Structures—0
Structured Convolutions for Efficient Neural Network Design—0
Iterative Compression of End-to-End ASR Model using AutoML—0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
Implicit Regularization via Neural Feature AlignmentCode1
Differentiable Feature Aggregation Search for Knowledge Distillation—0
Compressing Deep Neural Networks via Layer Fusion—0
ALF: Autoencoder-based Low-rank Filter-sharing for Efficient Convolutional Neural Networks—0
RT3D: Achieving Real-Time Execution of 3D Convolutional Neural Networks on Mobile Devices—0
FTRANS: Energy-Efficient Acceleration of Transformers using FPGA—0
Representation Transfer by Optimal Transport—0
Learning to Prune Deep Neural Networks via Reinforcement Learning—0
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks—0
Knowledge Distillation Beyond Model Compression—0
Self-Supervised GAN CompressionCode0
Channel Compression: Rethinking Information Redundancy among Channels in CNN Architecture—0
Go Wide, Then Narrow: Efficient Training of Deep Thin Networks—0
Exploring the Limits of Simple Learners in Knowledge Distillation for Document Classification with DocBERT—0
On the Demystification of Knowledge Distillation: A Residual Network Perspective—0
PFGDF: Pruning Filter via Gaussian Distribution Feature for Deep Neural Networks Acceleration—0
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization—0
Paying more attention to snapshots of Iterative Pruning: Improving Model Compression via Ensemble DistillationCode1
Improving Post Training Neural Quantization: Layer-wise Calibration and Integer ProgrammingCode1
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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