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 401–425 of 1356 papers

TitleStatusHype
USM-Lite: Quantization and Sparsity Aware Fine-tuning for Speech Recognition with Universal Speech Models—0
Rethinking Compression: Reduced Order Modelling of Latent Features in Large Language ModelsCode1
Neural Architecture Codesign for Fast Bragg Peak Analysis—0
Large Multimodal Model Compression via Efficient Pruning and Distillation at AntGroupCode0
Understanding the Effect of Model Compression on Social Bias in Large Language ModelsCode0
Language Model Knowledge Distillation for Efficient Question Answering in SpanishCode0
Physics Inspired Criterion for Pruning-Quantization Joint LearningCode0
The Efficiency Spectrum of Large Language Models: An Algorithmic SurveyCode0
LayerCollapse: Adaptive compression of neural networks—0
Privacy and Accuracy Implications of Model Complexity and Integration in Heterogeneous Federated LearningCode0
Towards Higher Ranks via Adversarial Weight Pruning—0
Relationship between Model Compression and Adversarial Robustness: A Review of Current Evidence—0
Cosine Similarity Knowledge Distillation for Individual Class Information Transfer—0
Knowledge Distillation Based Semantic Communications For Multiple Users—0
Education distillation:getting student models to learn in shcools—0
Efficient Transformer Knowledge Distillation: A Performance Review—0
Compact 3D Gaussian Representation for Radiance FieldCode2
Towards Better Parameter-Efficient Fine-Tuning for Large Language Models: A Position Paper—0
Shedding the Bits: Pushing the Boundaries of Quantization with Minifloats on FPGAs—0
Efficient Neural Networks for Tiny Machine Learning: A Comprehensive Review—0
LQ-LoRA: Low-rank Plus Quantized Matrix Decomposition for Efficient Language Model FinetuningCode1
On the Impact of Calibration Data in Post-training Quantization and Pruning—0
A Speed Odyssey for Deployable Quantization of LLMs—0
FedCode: Communication-Efficient Federated Learning via Transferring Codebooks—0
EPIM: Efficient Processing-In-Memory Accelerators based on Epitome—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