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 226–250 of 1356 papers

TitleStatusHype
2-bit Conformer quantization for automatic speech recognition—0
Approximability and Generalisation—0
CoLLD: Contrastive Layer-to-layer Distillation for Compressing Multilingual Pre-trained Speech Encoders—0
Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy—0
Additive Tree-Structured Covariance Function for Conditional Parameter Spaces in Bayesian Optimization—0
Collaborative Teacher-Student Learning via Multiple Knowledge Transfer—0
ADC/DAC-Free Analog Acceleration of Deep Neural Networks with Frequency Transformation—0
Towards Feature Distribution Alignment and Diversity Enhancement for Data-Free Quantization—0
Applications of Knowledge Distillation in Remote Sensing: A Survey—0
Communication-Efficient Distributed Online Learning with Kernels—0
Accelerating Inference and Language Model Fusion of Recurrent Neural Network Transducers via End-to-End 4-bit Quantization—0
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth—0
ClusComp: A Simple Paradigm for Model Compression and Efficient Finetuning—0
A Partial Regularization Method for Network Compression—0
Closed-Loop Neural Interfaces with Embedded Machine Learning—0
CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks—0
Compacting Deep Neural Networks for Internet of Things: Methods and Applications—0
Classification-based Quality Estimation: Small and Efficient Models for Real-world Applications—0
An Overview of Neural Network Compression—0
AdaSpring: Context-adaptive and Runtime-evolutionary Deep Model Compression for Mobile Applications—0
10K is Enough: An Ultra-Lightweight Binarized Network for Infrared Small-Target Detection—0
Context-aware deep model compression for edge cloud computing—0
Convolutional Neural Network Compression via Dynamic Parameter Rank Pruning—0
A Novel Architecture Slimming Method for Network Pruning and Knowledge Distillation—0
Adaptive Learning of Tensor Network Structures—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