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 1126–1150 of 1356 papers

TitleStatusHype
Lightweight Convolutional Representations for On-Device Natural Language Processing—0
Search for Better Students to Learn Distilled Knowledge—0
MT-BioNER: Multi-task Learning for Biomedical Named Entity Recognition using Deep Bidirectional Transformers—0
Small, Accurate, and Fast Vehicle Re-ID on the Edge: the SAFR Approach—0
SS-Auto: A Single-Shot, Automatic Structured Weight Pruning Framework of DNNs with Ultra-High Efficiency—0
A "Network Pruning Network" Approach to Deep Model Compression—0
FedBoost: A Communication-Efficient Algorithm for Federated Learning—0
PatDNN: Achieving Real-Time DNN Execution on Mobile Devices with Pattern-based Weight Pruning—0
Differentiable Architecture Compression—0
DeGAN : Data-Enriching GAN for Retrieving Representative Samples from a Trained Classifier—0
Domain Adaptation Regularization for Spectral Pruning—0
Data-Free Adversarial DistillationCode0
Pruning by Explaining: A Novel Criterion for Deep Neural Network PruningCode0
Towards Building a Real Time Mobile Device Bird Counting System Through Synthetic Data Training and Model Compression—0
An Improving Framework of regularization for Network Compression—0
Explaining Sequence-Level Knowledge Distillation as Data-Augmentation for Neural Machine Translation—0
Deep Model Compression Via Two-Stage Deep Reinforcement Learning—0
The Knowledge Within: Methods for Data-Free Model Compression—0
TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLPCode0
Exploring Unexplored Tensor Network Decompositions for Convolutional Neural NetworksCode0
Pruning at a Glance: Global Neural Pruning for Model Compression—0
Communication-Efficient Distributed Online Learning with Kernels—0
Data-Driven Compression of Convolutional Neural Networks—0
Structured Multi-Hashing for Model Compression—0
A SOT-MRAM-based Processing-In-Memory Engine for Highly Compressed DNN Implementation—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