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 701–750 of 1356 papers

TitleStatusHype
ResSVD: Residual Compensated SVD for Large Language Model Compression—0
Retraining-Based Iterative Weight Quantization for Deep Neural Networks—0
Retrieval-based Knowledge Transfer: An Effective Approach for Extreme Large Language Model Compression—0
Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice—0
Reversible Watermarking in Deep Convolutional Neural Networks for Integrity Authentication—0
Revisiting Data Augmentation in Model Compression: An Empirical and Comprehensive Study—0
Revisiting Self-Distillation—0
Reweighted Solutions for Weighted Low Rank Approximation—0
Riemannian Low-Rank Model Compression for Federated Learning with Over-the-Air Aggregation—0
RingMoE: Mixture-of-Modality-Experts Multi-Modal Foundation Models for Universal Remote Sensing Image Interpretation—0
RLRC: Reinforcement Learning-based Recovery for Compressed Vision-Language-Action Models—0
Robot Intent Recognition Method Based on State Grid Business Office—0
Robustness-Guided Image Synthesis for Data-Free Quantization—0
Robustness in Compressed Neural Networks for Object Detection—0
Robust testing of low-dimensional functions—0
Role of Mixup in Topological Persistence Based Knowledge Distillation for Wearable Sensor Data—0
Runtime Tunable Tsetlin Machines for Edge Inference on eFPGAs—0
SaleNet: A low-power end-to-end CNN accelerator for sustained attention level evaluation using EEG—0
Saten: Sparse Augmented Tensor Networks for Post-Training Compression of Large Language Models—0
Scalable Teacher Forcing Network for Semi-Supervised Large Scale Data Streams—0
Scaling Laws for Deep Learning—0
SCSP: Spectral Clustering Filter Pruning with Soft Self-adaption Manners—0
SDQ: Sparse Decomposed Quantization for LLM Inference—0
Search for Better Students to Learn Distilled Knowledge—0
SeKron: A Decomposition Method Supporting Many Factorization Structures—0
Selective Convolutional Units: Improving CNNs via Channel Selectivity—0
Self-calibration for Language Model Quantization and Pruning—0
Self-Supervised Generative Adversarial Compression—0
Efficient Personalized Speech Enhancement through Self-Supervised Learning—0
Semantic Retention and Extreme Compression in LLMs: Can We Have Both?—0
Semantics Prompting Data-Free Quantization for Low-Bit Vision Transformers—0
SEOFP-NET: Compression and Acceleration of Deep Neural Networks for Speech Enhancement Using Sign-Exponent-Only Floating-Points—0
Sequence-Level Knowledge Distillation for Model Compression of Attention-based Sequence-to-Sequence Speech Recognition—0
Encoding Weights of Irregular Sparsity for Fixed-to-Fixed Model Compression—0
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures—0
SGAD: Soft-Guided Adaptively-Dropped Neural Network—0
SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems—0
Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction—0
Shrinking Bigfoot: Reducing wav2vec 2.0 footprint—0
ShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning—0
Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing—0
Masked Training of Neural Networks with Partial Gradients—0
Small, Accurate, and Fast Vehicle Re-ID on the Edge: the SAFR Approach—0
Small Language Models: Architectures, Techniques, Evaluation, Problems and Future Adaptation—0
Small Object Detection Based on Modified FSSD and Model Compression—0
Smart Environmental Monitoring of Marine Pollution using Edge AI—0
SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation—0
Smooth Model Compression without Fine-Tuning—0
CrAFT: Compression-Aware Fine-Tuning for Efficient Visual Task Adaptation—0
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks—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