Papers Model Compression
“Model Compression” 태그가 달린 논문 1,632편 · 필터 해제
Expediting In-Network Federated Learning by Voting-Based Consensus Model Compression
Recently, federated learning (FL) has gained momentum because of its capability in preserving data privacy. To conduct model training by FL, multiple clients exchange model updates with a parameter server via Internet. T…
Federated LearningModel CompressionFed-CVLC: Compressing Federated Learning Communications with Variable-Length Codes
In Federated Learning (FL) paradigm, a parameter server (PS) concurrently communicates with distributed participating clients for model collection, update aggregation, and model distribution over multiple rounds, without…
Federated LearningModel CompressionQuantizationThe Potential of AutoML for Recommender Systems
Automated Machine Learning (AutoML) has greatly advanced applications of Machine Learning (ML) including model compression, machine translation, and computer vision. Recommender Systems (RecSys) can be seen as an applica…
AutoMLMachine TranslationModel CompressionRecommendation SystemsDynamic Sparse Learning: A Novel Paradigm for Efficient Recommendation
In the realm of deep learning-based recommendation systems, the increasing computational demands, driven by the growing number of users and items, pose a significant challenge to practical deployment. This challenge is p…
Model CompressionRecommendation SystemsSparse LearningA Survey on Transformer Compression
Transformer plays a vital role in the realms of natural language processing (NLP) and computer vision (CV), specially for constructing large language models (LLM) and large vision models (LVM). Model compression methods …
Knowledge DistillationMambaModel CompressionQuantization+1Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models
The growing digital landscape of fashion e-commerce calls for interactive and user-friendly interfaces for virtually trying on clothes. Traditional try-on methods grapple with challenges in adapting to diverse background…
Image GenerationModel CompressionVirtual Try-onFaster and Lighter LLMs: A Survey on Current Challenges and Way Forward
Despite the impressive performance of LLMs, their widespread adoption faces challenges due to substantial computational and memory requirements during inference. Recent advancements in model compression and system-level …
Model CompressionSurveyRADIN: Souping on a Budget
Model Soups, extending Stochastic Weights Averaging (SWA), combine models fine-tuned with different hyperparameters. Yet, their adoption is hindered by computational challenges due to subset selection issues. In this pap…
Ensemble LearningModel CompressionDiffusion Model Compression for Image-to-Image Translation
As recent advances in large-scale Text-to-Image (T2I) diffusion models have yielded remarkable high-quality image generation, diverse downstream Image-to-Image (I2I) applications have emerged. Despite the impressive resu…
Conditional Image GenerationDenoisingImage GenerationImage Restoration+4Effective Multi-Stage Training Model For Edge Computing Devices In Intrusion Detection
Intrusion detection poses a significant challenge within expansive and persistently interconnected environments. As malicious code continues to advance and sophisticated attack methodologies proliferate, various advanced…
Edge-computingIntrusion DetectionModel CompressionEPSD: Early Pruning with Self-Distillation for Efficient Model Compression
Neural network compression techniques, such as knowledge distillation (KD) and network pruning, have received increasing attention. Recent work `Prune, then Distill' reveals that a pruned student-friendly teacher network…
Knowledge DistillationModel CompressionNetwork PruningNeural Network CompressionSwapNet: Efficient Swapping for DNN Inference on Edge AI Devices Beyond the Memory Budget
Executing deep neural networks (DNNs) on edge artificial intelligence (AI) devices enables various autonomous mobile computing applications. However, the memory budget of edge AI devices restricts the number and complexi…
GPUModel CompressionSchedulingTQCompressor: improving tensor decomposition methods in neural networks via permutations
We introduce TQCompressor, a novel method for neural network model compression with improved tensor decompositions. We explore the challenges posed by the computational and storage demands of pre-trained language models …
Knowledge DistillationModel CompressionTensor DecompositionLiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object Detection
Due to highly constrained computing power and memory, deploying 3D lidar-based detectors on edge devices equipped in autonomous vehicles and robots poses a crucial challenge. Being a convenient and straightforward model …
3D Object DetectionAutonomous VehiclesModel Compressionobject-detection+2CompactifAI: Extreme Compression of Large Language Models using Quantum-Inspired Tensor Networks
Large Language Models (LLMs) such as ChatGPT and LlaMA are advancing rapidly in generative Artificial Intelligence (AI), but their immense size poses significant challenges, such as huge training and inference costs, sub…
Model CompressionQuantizationTensor NetworksCommunication-Efficient Federated Learning through Adaptive Weight Clustering and Server-Side Distillation
Federated Learning (FL) is a promising technique for the collaborative training of deep neural networks across multiple devices while preserving data privacy. Despite its potential benefits, FL is hindered by excessive c…
ClusteringFederated LearningKnowledge DistillationModel CompressionLarge receptive field strategy and important feature extraction strategy in 3D object detection
The enhancement of 3D object detection is pivotal for precise environmental perception and improved task execution capabilities in autonomous driving. LiDAR point clouds, offering accurate depth information, serve as a c…
3D Object DetectionAutonomous Drivingfeature selectionModel Compression+2Pruning for Protection: Increasing Jailbreak Resistance in Aligned LLMs Without Fine-Tuning
This paper investigates the impact of model compression on the way Large Language Models (LLMs) process prompts, particularly concerning jailbreak resistance. We show that moderate WANDA pruning can enhance resistance to…
Model CompressionModel Compression Techniques in Biometrics Applications: A Survey
The development of deep learning algorithms has extensively empowered humanity's task automatization capacity. However, the huge improvement in the performance of these models is highly correlated with their increasing l…
FairnessKnowledge DistillationmodelModel Compression+2SymbolNet: Neural Symbolic Regression with Adaptive Dynamic Pruning for Compression
Compact symbolic expressions have been shown to be more efficient than neural network models in terms of resource consumption and inference speed when implemented on custom hardware such as FPGAs, while maintaining compa…
Jet TaggingModel CompressionregressionSymbolic Regression