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 401–425 of 1356 papers

TitleStatusHype
Does Learning Require Memorization? A Short Tale about a Long Tail—0
Domain Adaptation Regularization for Spectral Pruning—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
Don't Be So Dense: Sparse-to-Sparse GAN Training Without Sacrificing Performance—0
Don't encrypt the data; just approximate the model \ Towards Secure Transaction and Fair Pricing of Training Data—0
Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network—0
Automatic Block-wise Pruning with Auxiliary Gating Structures for Deep Convolutional Neural Networks—0
Dream Distillation: A Data-Independent Model Compression Framework—0
Dreaming To Prune Image Deraining Networks—0
Stochastic Model Pruning via Weight Dropping Away and Back—0
Data-Model-Circuit Tri-Design for Ultra-Light Video Intelligence on Edge Devices—0
Data-Independent Structured Pruning of Neural Networks via Coresets—0
Automated Model Compression by Jointly Applied Pruning and Quantization—0
Dual sparse training framework: inducing activation map sparsity via Transformed 1 regularization—0
Can Model Compression Improve NLP Fairness—0
Dynamically Hierarchy Revolution: DirNet for Compressing Recurrent Neural Network on Mobile Devices—0
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models—0
Data-Free Quantization via Pseudo-label Filtering—0
Data-Free Quantization via Mixed-Precision Compensation without Fine-Tuning—0
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Automated Inference of Graph Transformation Rules—0
Dynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation—0
DynaQuant: Compressing Deep Learning Training Checkpoints via Dynamic Quantization—0
Data-Free Knowledge Transfer: A Survey—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