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 1076–1100 of 1356 papers

TitleStatusHype
One Teacher is Enough? Pre-trained Language Model Distillation from Multiple Teachers—0
One Weight Bitwidth to Rule Them All—0
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL—0
Online Cross-Layer Knowledge Distillation on Graph Neural Networks with Deep Supervision—0
Online Model Compression for Federated Learning with Large Models—0
On Multilingual Encoder Language Model Compression for Low-Resource Languages—0
On the Adversarial Robustness of Quantized Neural Networks—0
On the Compression of Recurrent Neural Networks with an Application to LVCSR acoustic modeling for Embedded Speech Recognition—0
On the Demystification of Knowledge Distillation: A Residual Network Perspective—0
On the Effectiveness of Low-Rank Matrix Factorization for LSTM Model Compression—0
On the Impact of Quantization and Pruning of Self-Supervised Speech Models for Downstream Speech Recognition Tasks "In-the-Wild''—0
On the social bias of speech self-supervised models—0
Optimal Policy Sparsification and Low Rank Decomposition for Deep Reinforcement Learning—0
Optimising TinyML with Quantization and Distillation of Transformer and Mamba Models for Indoor Localisation on Edge Devices—0
Optimization and Scalability of Collaborative Filtering Algorithms in Large Language Models—0
Optimize Deep Convolutional Neural Network with Ternarized Weights and High Accuracy—0
Optimizing LLMs for Resource-Constrained Environments: A Survey of Model Compression Techniques—0
Optimizing Singular Spectrum for Large Language Model Compression—0
Optimizing Small Language Models for In-Vehicle Function-Calling—0
Optimizing Traffic Signal Control using High-Dimensional State Representation and Efficient Deep Reinforcement Learning—0
OPTISHEAR: Towards Efficient and Adaptive Pruning of Large Language Models via Evolutionary Optimization—0
Oracle Teacher: Leveraging Target Information for Better Knowledge Distillation of CTC Models—0
OTOV2: Automatic, Generic, User-Friendly—0
Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling—0
Pacemaker: Intermediate Teacher Knowledge Distillation For On-The-Fly Convolutional Neural Network—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