Papers Model Compression
“Model Compression” 태그가 달린 논문 1,632편 · 필터 해제
Learning Disentangled Representation with Mutual Information Maximization for Real-Time UAV Tracking
Efficiency has been a critical problem in UAV tracking due to limitations in computation resources, battery capacity, and unmanned aerial vehicle maximum load. Although discriminative correlation filters (DCF)-based trac…
CPUModel CompressionRepresentation LearningAn Empirical Study of CLIP for Text-based Person Search
Text-based Person Search (TBPS) aims to retrieve the person images using natural language descriptions. Recently, Contrastive Language Image Pretraining (CLIP), a universal large cross-modal vision-language pre-training …
Cross-Modal RetrievalData AugmentationModel CompressionPerson Search+3Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey
Image restoration (IR) has been an indispensable and challenging task in the low-level vision field, which strives to improve the subjective quality of images distorted by various forms of degradation. Recently, the diff…
DeblurringImage RestorationImage Super-ResolutionModel Compression+1SHARK: A Lightweight Model Compression Approach for Large-scale Recommender Systems
Increasing the size of embedding layers has shown to be effective in improving the performance of recommendation models, yet gradually causing their sizes to exceed terabytes in industrial recommender systems, and hence …
Model CompressionQuantizationRecommendation SystemsSpike-and-slab shrinkage priors for structurally sparse Bayesian neural networks
Network complexity and computational efficiency have become increasingly significant aspects of deep learning. Sparse deep learning addresses these challenges by recovering a sparse representation of the underlying targe…
Computational EfficiencyModel CompressionVariational InferenceBenchmarking Adversarial Robustness of Compressed Deep Learning Models
The increasing size of Deep Neural Networks (DNNs) poses a pressing need for model compression, particularly when employed on resource constrained devices. Concurrently, the susceptibility of DNNs to adversarial attacks …
Adversarial RobustnessBenchmarkingDeep LearningModel CompressionA Survey on Model Compression for Large Language Models
Large Language Models (LLMs) have transformed natural language processing tasks successfully. Yet, their large size and high computational needs pose challenges for practical use, especially in resource-limited settings.…
BenchmarkingKnowledge DistillationModel CompressionQuantization+1Shortcut-V2V: Compression Framework for Video-to-Video Translation based on Temporal Redundancy Reduction
Video-to-video translation aims to generate video frames of a target domain from an input video. Despite its usefulness, the existing networks require enormous computations, necessitating their model compression for wide…
Computational EfficiencyModel CompressionTranslationFedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing
As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classificati…
Edge-computingFederated LearningModel CompressionTraffic ClassificationResource Constrained Model Compression via Minimax Optimization for Spiking Neural Networks
Brain-inspired Spiking Neural Networks (SNNs) have the characteristics of event-driven and high energy-efficient, which are different from traditional Artificial Neural Networks (ANNs) when deployed on edge devices such …
Model CompressionSparse LearningAccurate Retraining-free Pruning for Pretrained Encoder-based Language Models
Given a pretrained encoder-based language model, how can we accurately compress it without retraining? Retraining-free structured pruning algorithms are crucial in pretrained language model compression due to their signi…
Language ModelingLanguage ModellingModel CompressionAccurate Neural Network Pruning Requires Rethinking Sparse Optimization
Obtaining versions of deep neural networks that are both highly-accurate and highly-sparse is one of the main challenges in the area of model compression, and several high-performance pruning techniques have been investi…
Model CompressionNetwork PruningStochastic OptimizationMIMONet: Multi-Input Multi-Output On-Device Deep Learning
Future intelligent robots are expected to process multiple inputs simultaneously (such as image and audio data) and generate multiple outputs accordingly (such as gender and emotion), similar to humans. Recent research h…
Deep LearningModel CompressionModel Compression Methods for YOLOv5: A Review
Over the past few years, extensive research has been devoted to enhancing YOLO object detectors. Since its introduction, eight major versions of YOLO have been introduced with the purpose of improving its accuracy and ef…
Knowledge DistillationmodelModel CompressionNetwork Pruning+2Impact of Disentanglement on Pruning Neural Networks
Deploying deep learning neural networks on edge devices, to accomplish task specific objectives in the real-world, requires a reduction in their memory footprint, power consumption, and latency. This can be realized via …
DisentanglementModel CompressionKnowledge Distillation for Object Detection: from generic to remote sensing datasets
Knowledge distillation, a well-known model compression technique, is an active research area in both computer vision and remote sensing communities. In this paper, we evaluate in a remote sensing context various off-the-…
Knowledge DistillationModel Compressionobject-detectionObject Detection+2CA-LoRA: Adapting Existing LoRA for Compressed LLMs to Enable Efficient Multi-Tasking on Personal Devices
Recently, there has been a demand to deploy Large Language Models (LLMs) on personal devices such as laptops and smartphones. These LLMs have different model variants when handling different tasks. However, personal devi…
Model CompressionDistilling Universal and Joint Knowledge for Cross-Domain Model Compression on Time Series Data
For many real-world time series tasks, the computational complexity of prevalent deep leaning models often hinders the deployment on resource-limited environments (e.g., smartphones). Moreover, due to the inevitable doma…
Knowledge DistillationModel CompressionTime SeriesTransfer LearningDistilled Pruning: Using Synthetic Data to Win the Lottery
This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic optimization, our method reconsiders the…
Efficient Neural NetworkModel CompressionNetwork PruningNeural Architecture SearchData-Free Quantization via Mixed-Precision Compensation without Fine-Tuning
Neural network quantization is a very promising solution in the field of model compression, but its resulting accuracy highly depends on a training/fine-tuning process and requires the original data. This not only brings…
Data Free QuantizationModel CompressionQuantization