Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Communication-Efficient Large-Scale Distributed Deep Learning: A Comprehensive Survey
With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning. In contrast to traditional distributed deep le…
Data CompressionDeep LearningSchedulingSurveyPower-Efficient Image Storage: Leveraging Super Resolution Generative Adversarial Network for Sustainable Compression and Reduced Carbon Footprint
In recent years, large-scale adoption of cloud storage solutions has revolutionized the way we think about digital data storage. However, the exponential increase in data volume, especially images, has raised environment…
Data CompressionGenerative Adversarial NetworkImage CompressionSSIM+1Optimizing traffic signs and lights visibility for the teleoperation of autonomous vehicles through ROI compression
Autonomous vehicles are a promising solution to traffic congestion, air pollution, accidents, and wasted time and resources. However, remote driver intervention may be necessary for extreme situations to ensure safe road…
Autonomous VehiclesData CompressionSemantic SegmentationComputationally and Memory-Efficient Robust Predictive Analytics Using Big Data
In the current data-intensive era, big data has become a significant asset for Artificial Intelligence (AI), serving as a foundation for developing data-driven models and providing insight into various unknown fields. Th…
Data CompressionUnderstanding The Effectiveness of Lossy Compression in Machine Learning Training Sets
Learning and Artificial Intelligence (ML/AI) techniques have become increasingly prevalent in high performance computing (HPC). However, these methods depend on vast volumes of floating point data for training and valida…
Data CompressionApplication of Tensorized Neural Networks for Cloud Classification
Convolutional neural networks (CNNs) have gained widespread usage across various fields such as weather forecasting, computer vision, autonomous driving, and medical image analysis due to its exceptional ability to extra…
Autonomous DrivingClassificationData CompressionMedical Image Analysis+2SketchINR: A First Look into Sketches as Implicit Neural Representations
We propose SketchINR, to advance the representation of vector sketches with implicit neural models. A variable length vector sketch is compressed into a latent space of fixed dimension that implicitly encodes the underly…
Data CompressionDecoderGrid Monitoring with Synchro-Waveform and AI Foundation Model Technologies
Purpose:This article advocates for the development of a next-generation grid monitoring and control system designed for future grids dominated by inverter-based resources. Leveraging recent progress in generative artific…
Anomaly DetectionData CompressionFault DetectionTime SeriesProbing Image Compression For Class-Incremental Learning
Image compression emerges as a pivotal tool in the efficient handling and transmission of digital images. Its ability to substantially reduce file size not only facilitates enhanced data storage capacity but also potenti…
class-incremental learningClass Incremental LearningData Compressionimage-classification+3Blockchain-Enabled Variational Information Bottleneck for IoT Networks
In Internet of Things (IoT) networks, the amount of data sensed by user devices may be huge, resulting in the serious network congestion. To solve this problem, intelligent data compression is critical. The variational i…
CPUData CompressionDecoderOn the Compressibility of Quantized Large Language Models
Deploying Large Language Models (LLMs) on edge or mobile devices offers significant benefits, such as enhanced data privacy and real-time processing capabilities. However, it also faces critical challenges due to the sub…
Data CompressionQuantizationTokenization Is More Than Compression
Tokenization is a foundational step in natural language processing (NLP) tasks, bridging raw text and language models. Existing tokenization approaches like Byte-Pair Encoding (BPE) originate from the field of data compr…
Data CompressionTowards Optimal Learning of Language Models
This work studies the general principles of improving the learning of language models (LMs), which aims at reducing the necessary training steps for achieving superior performance. Specifically, we present a theory for t…
Data CompressionLanguage ModelingLanguage ModellingDistortion-Controlled Dithering with Reduced Recompression Rate
Dithering is a technique that can improve human perception of low-resolution data by reducing quantization artifacts. In this work we formalize and analytically justify two metrics for quantization artifact prominence, u…
Data CompressionImage CompressionQuantizationMechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage
Structural health monitoring (SHM) ensures the safety and longevity of structures like buildings and bridges. As the volume and scale of structures and the impact of their failure continue to grow, there is a dire need f…
Data CompressionStructural Health Monitoring3D Point Cloud Compression with Recurrent Neural Network and Image Compression Methods
Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the sparsity and unordered structure of the…
Data CompressionImage CompressionCompression of Structured Data with Autoencoders: Provable Benefit of Nonlinearities and Depth
Autoencoders are a prominent model in many empirical branches of machine learning and lossy data compression. However, basic theoretical questions remain unanswered even in a shallow two-layer setting. In particular, to …
Data CompressionDecoderDenoisingTexShape: Information Theoretic Sentence Embedding for Language Models
With the exponential growth in data volume and the emergence of data-intensive applications, particularly in the field of machine learning, concerns related to resource utilization, privacy, and fairness have become para…
Data CompressionFairnessLanguage ModelingLanguage Modelling+3Generative Visual Compression: A Review
Artificial Intelligence Generated Content (AIGC) is leading a new technical revolution for the acquisition of digital content and impelling the progress of visual compression towards competitive performance gains and div…
Data CompressionFlexible Variational Information Bottleneck: Achieving Diverse Compression with a Single Training
Information Bottleneck (IB) is a widely used framework that enables the extraction of information related to a target random variable from a source random variable. In the objective function, IB controls the trade-off be…
Data Compression