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

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Showing 751–800 of 1356 papers

TitleStatusHype
Comprehensive Knowledge Distillation with Causal InterventionCode1
Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations—0
Aligned Structured Sparsity Learning for Efficient Image Super-ResolutionCode1
A Unified Pruning Framework for Vision TransformersCode1
FedHM: Efficient Federated Learning for Heterogeneous Models via Low-rank Factorization—0
Exploring Low-Cost Transformer Model Compression for Large-Scale Commercial Reply Suggestions—0
Accelerating Deep Learning with Dynamic Data Pruning—0
NAM: Normalization-based Attention ModuleCode1
Sharpness-aware Quantization for Deep Neural NetworksCode1
Semi-Online Knowledge DistillationCode0
Automatic Mapping of the Best-Suited DNN Pruning Schemes for Real-Time Mobile Acceleration—0
Local-Selective Feature Distillation for Single Image Super-Resolution—0
Structured Pruning Learns Compact and Accurate Models—0
Weight Squeezing: Reparameterization for Knowledge Transfer and Model Compression—0
Learning-Based Symbol Level Precoding: A Memory-Efficient Unsupervised Learning Approach—0
Learning Interpretation with Explainable Knowledge Distillation—0
Domain Generalization on Efficient Acoustic Scene Classification using Residual Normalization—0
A Survey on Green Deep Learning—0
SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points—0
LiMuSE: Lightweight Multi-modal Speaker ExtractionCode1
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models—0
Weight, Block or Unit? Exploring Sparsity Tradeoffs for Speech Enhancement on Tiny Neural Accelerators—0
How to Select One Among All ? An Empirical Study Towards the Robustness of Knowledge Distillation in Natural Language Understanding—0
Distilling Object Detectors with Feature RichnessCode1
ILMPQ : An Intra-Layer Multi-Precision Deep Neural Network Quantization framework for FPGA—0
On Cross-Layer Alignment for Model Fusion of Heterogeneous Neural Networks—0
Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural NetworksCode1
Reconstructing Pruned Filters using Cheap Spatial Transformations—0
Exploring Gradient Flow Based Saliency for DNN Model CompressionCode0
How and When Adversarial Robustness Transfers in Knowledge Distillation?—0
Analysis of memory consumption by neural networks based on hyperparameters—0
Augmenting Knowledge Distillation With Peer-To-Peer Mutual Learning For Model Compression—0
Accelerating Framework of Transformer by Hardware Design and Model Compression Co-Optimization—0
Pro-KD: Progressive Distillation by Following the Footsteps of the Teacher—0
A Short Study on Compressing Decoder-Based Language Models—0
HRKD: Hierarchical Relational Knowledge Distillation for Cross-domain Language Model CompressionCode0
Robustness Challenges in Model Distillation and Pruning for Natural Language Understanding—0
Differentiable Network Pruning for Microcontrollers—0
Joint Channel and Weight Pruning for Model Acceleration on Moblie DevicesCode1
Kronecker Decomposition for GPT Compression—0
A Memory-Efficient Learning Framework for SymbolLevel Precoding with Quantized NN Weights—0
Rectifying the Data Bias in Knowledge Distillation—0
FedDQ: Communication-Efficient Federated Learning with Descending Quantization—0
Robot Intent Recognition Method Based on State Grid Business Office—0
A Unified Knowledge Distillation Framework for Deep Directed Graphical Models—0
KIMERA: Injecting Domain Knowledge into Vacant Transformer Heads—0
Sparse Unbalanced GAN Training with In-Time Over-Parameterization—0
Prototypical Contrastive Predictive Coding—0
Model Compression via Symmetries of the Parameter Space—0
Learning Efficient Image Super-Resolution Networks via Structure-Regularized Pruning—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