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 1001–1025 of 1356 papers

TitleStatusHype
AutoBSS: An Efficient Algorithm for Block Stacking Style Search—0
Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher—0
Noisy Neural Network Compression for Analog Storage Devices—0
Towards Compact Neural Networks via End-to-End Training: A Bayesian Tensor Approach with Automatic Rank DeterminationCode1
Closed-Loop Neural Interfaces with Embedded Machine Learning—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
BERT-EMD: Many-to-Many Layer Mapping for BERT Compression with Earth Mover's DistanceCode1
A Model Compression Method with Matrix Product Operators for Speech Enhancement—0
Compressing Deep Convolutional Neural Networks by Stacking Low-dimensional Binary Convolution Filters—0
GECKO: Reconciling Privacy, Accuracy and Efficiency in Embedded Deep Learning—0
Pea-KD: Parameter-efficient and Accurate Knowledge Distillation on BERT—0
Improved Knowledge Distillation via Full Kernel Matrix TransferCode0
Contrastive Distillation on Intermediate Representations for Language Model CompressionCode1
Pea-KD: Parameter-efficient and accurate Knowledge Distillation—0
Conditional Automated Channel Pruning for Deep Neural Networks—0
Densely Guided Knowledge Distillation using Multiple Teacher AssistantsCode1
MSP: An FPGA-Specific Mixed-Scheme, Multi-Precision Deep Neural Network Quantization Framework—0
A Progressive Sub-Network Searching Framework for Dynamic Inference—0
Binary Classification as a Phase Separation ProcessCode0
Compression-aware Continual Learning using Singular Value DecompositionCode0
A Partial Regularization Method for Network Compression—0
Layer-specific Optimization for Mixed Data Flow with Mixed Precision in FPGA Design for CNN-based Object Detectors—0
Against Membership Inference Attack: Pruning is All You Need—0
One Weight Bitwidth to Rule Them All—0
Data-Independent Structured Pruning of Neural Networks via Coresets—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