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 451–475 of 1356 papers

TitleStatusHype
Model Adaptation for Time Constrained Embodied Control—0
An Empirical Investigation of Matrix Factorization Methods for Pre-trained Transformers—0
Compress then Serve: Serving Thousands of LoRA Adapters with Little Overhead—0
Knowledge Distillation in Federated Learning: a Survey on Long Lasting Challenges and New Solutions—0
Implicit Neural Representation for Videos Based on Residual Connection—0
PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation—0
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters—0
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications—0
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases—0
On the social bias of speech self-supervised models—0
Slicing Mutual Information Generalization Bounds for Neural NetworksCode0
Enhancing In-Context Learning Performance with just SVD-Based Weight Pruning: A Theoretical PerspectiveCode0
Reweighted Solutions for Weighted Low Rank Approximation—0
Towards Efficient Deep Spiking Neural Networks Construction with Spiking Activity based Pruning—0
Robust Knowledge Distillation Based on Feature Variance Against Backdoored Teacher ModelCode0
Effective Interplay between Sparsity and Quantization: From Theory to Practice—0
LCQ: Low-Rank Codebook based Quantization for Large Language Models—0
Occam Gradient DescentCode0
Dual sparse training framework: inducing activation map sparsity via Transformed 1 regularization—0
subMFL: Compatiple subModel Generation for Federated Learning in Device Heterogenous EnvironmentCode0
Efficient Model Compression for Hierarchical Federated Learning—0
ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers—0
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models—0
Efficiency optimization of large-scale language models based on deep learning in natural language processing tasks—0
TinyM^2Net-V3: Memory-Aware Compressed Multimodal Deep Neural Networks for Sustainable Edge Deployment—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