Papers Model Compression
“Model Compression” 태그가 달린 논문 1,632편 · 필터 해제
MPruner: Optimizing Neural Network Size with CKA-Based Mutual Information Pruning
Determining the optimal size of a neural network is critical, as it directly impacts runtime performance and memory usage. Pruning is a well-established model compression technique that reduces the size of neural network…
Model CompressionLocalize-and-Stitch: Efficient Model Merging via Sparse Task Arithmetic
Model merging offers an effective strategy to combine the strengths of multiple finetuned models into a unified model that preserves the specialized capabilities of each. Existing methods merge models in a global manner,…
Model CompressionTask ArithmeticA Survey on Drowsiness Detection -- Modern Applications and Methods
Drowsiness detection holds paramount importance in ensuring safety in workplaces or behind the wheel, enhancing productivity, and healthcare across diverse domains. Therefore accurate and real-time drowsiness detection p…
Model CompressionSurveyA Web-Based Solution for Federated Learning with LLM-Based Automation
Federated Learning (FL) offers a promising approach for collaborative machine learning across distributed devices. However, its adoption is hindered by the complexity of building reliable communication architectures and …
CPUFederated LearningHyperparameter OptimizationLanguage Modelling+3Pruning By Explaining Revisited: Optimizing Attribution Methods to Prune CNNs and Transformers
To solve ever more complex problems, Deep Neural Networks are scaled to billions of parameters, leading to huge computational costs. An effective approach to reduce computational requirements and increase efficiency is t…
Model CompressionFine-Tuning and Deploying Large Language Models Over Edges: Issues and Approaches
Since the invention of GPT2--1.5B in 2019, large language models (LLMs) have transitioned from specialized models to versatile foundation models. The LLMs exhibit impressive zero-shot ability, however, require fine-tunin…
GPUModel CompressionNeR-VCP: A Video Content Protection Method Based on Implicit Neural Representation
With the popularity of video applications, the security of video content has emerged as a pressing issue that demands urgent attention. Most video content protection methods mainly rely on encryption technology, which ne…
Model CompressionNERMoDeGPT: Modular Decomposition for Large Language Model Compression
Large Language Models (LLMs) have reshaped the landscape of artificial intelligence by demonstrating exceptional performance across various tasks. However, substantial computational requirements make their deployment cha…
GPULanguage ModelingLanguage ModellingLarge Language Model+2RepControlNet: ControlNet Reparameterization
With the wide application of diffusion model, the high cost of inference resources has became an important bottleneck for its universal application. Controllable generation, such as ControlNet, is one of the key research…
Model CompressionABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models
Large Language Models (LLMs) have revolutionized natural language processing tasks. However, their practical application is constrained by substantial memory and computational demands. Post-training quantization (PTQ) is…
GPUModel CompressionQuantizationComputer Vision Model Compression Techniques for Embedded Systems: A Survey
Deep neural networks have consistently represented the state of the art in most computer vision problems. In these scenarios, larger and more complex models have demonstrated superior performance to smaller architectures…
Model CompressionSurveyKnowledge Distillation with Refined Logits
Recent research on knowledge distillation has increasingly focused on logit distillation because of its simplicity, effectiveness, and versatility in model compression. In this paper, we introduce Refined Logit Distillat…
Knowledge DistillationModel CompressionInfra-YOLO: Efficient Neural Network Structure with Model Compression for Real-Time Infrared Small Object Detection
Although convolutional neural networks have made outstanding achievements in visible light target detection, there are still many challenges in infrared small object detection because of the low signal-to-noise ratio, in…
Efficient Neural NetworkModel CompressionObjectobject-detection+2An Effective Information Theoretic Framework for Channel Pruning
Channel pruning is a promising method for accelerating and compressing convolutional neural networks. However, current pruning algorithms still remain unsolved problems that how to assign layer-wise pruning ratios proper…
Model CompressionAdapMTL: Adaptive Pruning Framework for Multitask Learning Model
In the domain of multimedia and multimodal processing, the efficient handling of diverse data streams such as images, video, and sensor data is paramount. Model compression and multitask learning (MTL) are crucial in thi…
modelModel CompressionCompact 3D Gaussian Splatting for Static and Dynamic Radiance Fields
3D Gaussian splatting (3DGS) has recently emerged as an alternative representation that leverages a 3D Gaussian-based representation and introduces an approximated volumetric rendering, achieving very fast rendering spee…
3DGSModel CompressionQuantizationCompress and Compare: Interactively Evaluating Efficiency and Behavior Across ML Model Compression Experiments
To deploy machine learning models on-device, practitioners use compression algorithms to shrink and speed up models while maintaining their high-quality output. A critical aspect of compression in practice is model compa…
image-classificationImage ClassificationModel CompressionComb, Prune, Distill: Towards Unified Pruning for Vision Model Compression
Lightweight and effective models are essential for devices with limited resources, such as intelligent vehicles. Structured pruning offers a promising approach to model compression and efficiency enhancement. However, ex…
image-classificationImage ClassificationKnowledge DistillationModel Compression+1DopQ-ViT: Towards Distribution-Friendly and Outlier-Aware Post-Training Quantization for Vision Transformers
Vision transformers (ViTs) have garnered significant attention for their performance in vision tasks, but the high computational cost and significant latency issues have hindered widespread adoption. Post-training quanti…
Model CompressionQuantizationTensor Train Low-rank Approximation (TT-LoRA): Democratizing AI with Accelerated LLMs
In recent years, Large Language Models (LLMs) have demonstrated remarkable capabilities across a wide range of natural language processing (NLP) tasks, such as question-answering, sentiment analysis, text summarization, …
Machine TranslationModel Compressionparameter-efficient fine-tuningQuestion Answering+2