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 1051–1100 of 1356 papers

TitleStatusHype
Rank-Based Filter Pruning for Real-Time UAV Tracking—0
RankDVQA-mini: Knowledge Distillation-Driven Deep Video Quality Assessment—0
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization—0
Rate Distortion For Model Compression: From Theory To Practice—0
Experimental implementation of a neural network optical channel equalizer in restricted hardware using pruning and quantization—0
Real time backbone for semantic segmentation—0
TrimLLM: Progressive Layer Dropping for Domain-Specific LLMs—0
Membership Privacy for Machine Learning Models Through Knowledge Transfer—0
Rectifying the Data Bias in Knowledge Distillation—0
Recurrent Convolution for Compact and Cost-Adjustable Neural Networks: An Empirical Study—0
Recurrent Convolutions: A Model Compression Point of View—0
Trimming Down Large Spiking Vision Transformers via Heterogeneous Quantization Search—0
Reducing Computational Complexity of Neural Networks in Optical Channel Equalization: From Concepts to Implementation—0
What Makes a Good Dataset for Knowledge Distillation?—0
Reinforced Multi-Teacher Selection for Knowledge Distillation—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
A Deep Cascade Network for Unaligned Face Attribute Classification—0
Triple Sparsification of Graph Convolutional Networks without Sacrificing the Accuracy—0
Re-Parameterization of Lightweight Transformer for On-Device Speech Emotion Recognition—0
RepControlNet: ControlNet Reparameterization—0
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization—0
Representation Transfer by Optimal Transport—0
Tuning Algorithms and Generators for Efficient Edge Inference—0
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs—0
Residual Knowledge Distillation—0
ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification—0
Resource Allocation for Compression-aided Federated Learning with High Distortion Rate—0
TutorNet: Towards Flexible Knowledge Distillation for End-to-End Speech Recognition—0
ResSVD: Residual Compensated SVD for Large Language Model Compression—0
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
Retraining-Based Iterative Weight Quantization for Deep Neural Networks—0
AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications—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
Adaptive Learning of Tensor Network Structures—0
Robot Intent Recognition Method Based on State Grid Business Office—0
TwinDNN: A Tale of Two Deep Neural Networks—0
Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices—0
Two is Better than One: Efficient Ensemble Defense for Robust and Compact Models—0
Two-Pass End-to-End ASR Model Compression—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
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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