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 951–1000 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
Lottery Ticket Preserves Weight Correlation: Is It Desirable or Not?—0
Neural Network Compression for Noisy Storage Devices—0
Robustness in Compressed Neural Networks for Object Detection—0
Compressed Object DetectionCode0
It's always personal: Using Early Exits for Efficient On-Device CNN Personalisation—0
AACP: Model Compression by Accurate and Automatic Channel Pruning—0
Deep Model Compression based on the Training History—0
AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications—0
Differential Privacy Meets Federated Learning under Communication Constraints—0
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—0
Deep Compression of Neural Networks for Fault Detection on Tennessee Eastman Chemical Processes—0
Model Compression for Domain Adaptation through Causal Effect EstimationCode0
Single-path Bit Sharing for Automatic Loss-aware Model Compression—0
Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks—0
Adversarially Robust and Explainable Model Compression with On-Device Personalization for Text Classification—0
On-Device Document Classification using multimodal features—0
Block Skim Transformer for Efficient Question Answering—0
TwinDNN: A Tale of Two Deep Neural Networks—0
Model Compression via Hyper-Structure Network—0
Can Students Outperform Teachers in Knowledge Distillation based Model Compression?—0
Knowledge distillation via softmax regression representation learning—0
SACoD: Sensor Algorithm Co-Design Towards Efficient CNN-powered Intelligent PhlatCamCode0
Dynamic Probabilistic Pruning: Training sparse networks based on stochastic and dynamic masking—0
Exploration and Estimation for Model Compression—0
Post-Training Weighted Quantization of Neural Networks for Language Models—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