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 951–975 of 1356 papers

TitleStatusHype
Modulating Regularization Frequency for Efficient Compression-Aware Model Training—0
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression—0
On the Adversarial Robustness of Quantized Neural Networks—0
Knowledge Distillation for Swedish NER models: A Search for Performance and Efficiency—0
Stealthy Backdoors as Compression ArtifactsCode0
Spirit Distillation: A Model Compression Method with Multi-domain Knowledge Transfer—0
Spatio-Temporal Pruning and Quantization for Low-latency Spiking Neural Networks—0
Knowledge Distillation as Semiparametric InferenceCode0
Compact CNN Structure Learning by Knowledge Distillation—0
Augmenting Deep Classifiers with Polynomial Neural NetworksCode0
Annealing Knowledge DistillationCode0
Dual Discriminator Adversarial Distillation for Data-free Model Compression—0
Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication—0
Model Compression for Dynamic Forecast CombinationCode0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
Tight Compression: Compressing CNN Through Fine-Grained Pruning and Weight Permutation for Efficient Implementation—0
Shrinking Bigfoot: Reducing wav2vec 2.0 footprint—0
Prototype-based Personalized Pruning—0
Compacting Deep Neural Networks for Internet of Things: Methods and Applications—0
Robust Model Compression Using Deep HypothesesCode0
MWQ: Multiscale Wavelet Quantized Neural Networks—0
Formalizing Generalization and Robustness of Neural Networks to Weight Perturbations—0
On the Utility of Gradient Compression in Distributed Training SystemsCode0
PURSUhInT: In Search of Informative Hint Points Based on Layer Clustering for Knowledge Distillation—0
Preserved central model for faster bidirectional compression in distributed settingsCode0
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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