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 651–700 of 1356 papers

TitleStatusHype
PruMUX: Augmenting Data Multiplexing with Model CompressionCode0
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models—0
Selective Pre-training for Private Fine-tuningCode0
Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study—0
Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Prompt—0
Towards Understanding and Improving Knowledge Distillation for Neural Machine TranslationCode0
GSB: Group Superposition Binarization for Vision Transformer with Limited Training SamplesCode0
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation—0
Redundancy and Concept Analysis for Code-trained Language Models—0
CORSD: Class-Oriented Relational Self Distillation—0
Guaranteed Quantization Error Computation for Neural Network Model Compression—0
Bias in Pruned Vision Models: In-Depth Analysis and Countermeasures—0
Deep Collective Knowledge Distillation—0
Learning Accurate Performance Predictors for Ultrafast Automated Model CompressionCode0
Structured Pruning for Multi-Task Deep Neural Networks—0
Surrogate Lagrangian Relaxation: A Path To Retrain-free Deep Neural Network Pruning—0
oBERTa: Improving Sparse Transfer Learning via improved initialization, distillation, and pruning regimes—0
Information-Theoretic GAN Compression with Variational Energy-based Model—0
A Multi-objective Complex Network Pruning Framework Based on Divide-and-conquer and Global Performance Impairment Ranking—0
Tetra-AML: Automatic Machine Learning via Tensor Networks—0
Towards Accurate Post-Training Quantization for Vision Transformer—0
Exploring Turkish Speech Recognition via Hybrid CTC/Attention Architecture and Multi-feature Fusion Network—0
Low Rank Optimization for Efficient Deep Learning: Making A Balance between Compact Architecture and Fast Training—0
I3D: Transformer architectures with input-dependent dynamic depth for speech recognitionCode0
R2 Loss: Range Restriction Loss for Model Compression and Quantization—0
A Contrastive Knowledge Transfer Framework for Model Compression and Transfer LearningCode0
OTOV2: Automatic, Generic, User-Friendly—0
On Model Compression for Neural Networks: Framework, Algorithm, and Convergence GuaranteeCode0
Greener yet Powerful: Taming Large Code Generation Models with Quantization—0
Gradient-Free Structured Pruning with Unlabeled Data—0
Rotation Invariant Quantization for Model CompressionCode0
Adversarial Attacks on Machine Learning in Embedded and IoT Platforms—0
Towards domain generalisation in ASR with elitist sampling and ensemble knowledge distillation—0
Debiased Distillation by Transplanting the Last Layer—0
Structured Bayesian Compression for Deep Neural Networks Based on The Turbo-VBI Approach—0
HomoDistil: Homotopic Task-Agnostic Distillation of Pre-trained Transformers—0
A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques—0
Towards Optimal Compression: Joint Pruning and Quantization—0
On Achieving Privacy-Preserving State-of-the-Art Edge Intelligence—0
Knowledge Distillation in Vision Transformers: A Critical Review—0
Generalized Uncertainty of Deep Neural Networks: Taxonomy and Applications—0
Knowledge Distillation on Graphs: A Survey—0
AMD: Adaptive Masked Distillation for Object Detection—0
Improved knowledge distillation by utilizing backward pass knowledge in neural networks—0
HALOC: Hardware-Aware Automatic Low-Rank Compression for Compact Neural Networks—0
Accelerating and Compressing Deep Neural Networks for Massive MIMO CSI FeedbackCode0
HCE: Improving Performance and Efficiency with Heterogeneously Compressed Neural Network Ensemble—0
Distilling Focal Knowledge From Imperfect Expert for 3D Object DetectionCode0
One-Shot Model for Mixed-Precision Quantization—0
Tiny Updater: Towards Efficient Neural Network-Driven Software UpdatingCode0
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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