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 451–500 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
EncCluster: Scalable Functional Encryption in Federated Learning through Weight Clustering and Probabilistic Filters—0
PC-LoRA: Low-Rank Adaptation for Progressive Model Compression with Knowledge Distillation—0
MobileAIBench: Benchmarking LLMs and LMMs for On-Device Use Cases—0
DistilDoc: Knowledge Distillation for Visually-Rich Document Applications—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
Dual sparse training framework: inducing activation map sparsity via Transformed 1 regularization—0
Occam Gradient DescentCode0
subMFL: Compatiple subModel Generation for Federated Learning in Device Heterogenous EnvironmentCode0
NV-Embed: Improved Techniques for Training LLMs as Generalist Embedding Models—0
Efficient Model Compression for Hierarchical Federated Learning—0
ExtremeMETA: High-speed Lightweight Image Segmentation Model by Remodeling Multi-channel Metamaterial Imagers—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
Densely Distilling Cumulative Knowledge for Continual Learning—0
AdaKD: Dynamic Knowledge Distillation of ASR models using Adaptive Loss Weighting—0
Characterizing the Accuracy -- Efficiency Trade-off of Low-rank Decomposition in Language Models—0
NurtureNet: A Multi-task Video-based Approach for Newborn Anthropometry—0
From Algorithm to Hardware: A Survey on Efficient and Safe Deployment of Deep Neural Networks—0
Light Field Compression Based on Implicit Neural Representation—0
Communication-Efficient Federated Learning with Adaptive Compression under Dynamic Bandwidth—0
Trio-ViT: Post-Training Quantization and Acceleration for Softmax-Free Efficient Vision TransformerCode0
Iterative Filter Pruning for Concatenation-based CNN ArchitecturesCode0
Dependency-Aware Semi-Structured Sparsity of GLU Variants in Large Language Models—0
FedGreen: Carbon-aware Federated Learning with Model Size Adaptation—0
Rapid Deployment of DNNs for Edge Computing via Structured Pruning at Initialization—0
Data-free Knowledge Distillation for Fine-grained Visual CategorizationCode0
Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads—0
Comprehensive Survey of Model Compression and Speed up for Vision Transformers—0
Structured Model Pruning for Efficient Inference in Computational Pathology—0
Simplifying Two-Stage Detectors for On-Device Inference in Remote Sensing—0
Bayesian Federated Model Compression for Communication and Computation Efficiency—0
Multilingual Brain Surgeon: Large Language Models Can be Compressed Leaving No Language BehindCode0
Improve Knowledge Distillation via Label Revision and Data Selection—0
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution—0
Automated Inference of Graph Transformation Rules—0
On Linearizing Structured Data in Encoder-Decoder Language Models: Insights from Text-to-SQL—0
Enhancing Inference Efficiency of Large Language Models: Investigating Optimization Strategies and Architectural Innovations—0
Instance-Aware Group Quantization for Vision Transformers—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