Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Artificial Intelligence in Creative Industries: Advances Prior to 2025
The rapid advancements in artificial intelligence (AI), particularly in generative AI and large language models (LLMs), have profoundly impacted the creative industries by enabling innovative content creation, enhancing …
Data Compressionmultimodal generationNavigateRemote Inference over Dynamic Links via Adaptive Rate Deep Task-Oriented Vector Quantization
A broad range of technologies rely on remote inference, wherein data acquired is conveyed over a communication channel for inference in a remote server. Communication between the participating entities is often carried o…
Data CompressionQuantizationFoundation Model for Lossy Compression of Spatiotemporal Scientific Data
We present a foundation model (FM) for lossy scientific data compression, combining a variational autoencoder (VAE) with a hyper-prior structure and a super-resolution (SR) module. The VAE framework uses hyper-priors to …
Data CompressionSuper-ResolutionL3TC: Leveraging RWKV for Learned Lossless Low-Complexity Text Compression
Learning-based probabilistic models can be combined with an entropy coder for data compression. However, due to the high complexity of learning-based models, their practical application as text compressors has been large…
Data CompressionText CompressionQuantum Implicit Neural Compression
Signal compression based on implicit neural representation (INR) is an emerging technique to represent multimedia signals with a small number of bits. While INR-based signal compression achieves high-quality reconstructi…
Data CompressionImage CompressionLearned Data Compression: Challenges and Opportunities for the Future
Compressing integer keys is a fundamental operation among multiple communities, such as database management (DB), information retrieval (IR), and high-performance computing (HPC). Recent advances in \emph{learned indexes…
CPUData CompressionInformation RetrievalMulti-Scale Node Embeddings for Graph Modeling and Generation
Lying at the interface between Network Science and Machine Learning, node embedding algorithms take a graph as input and encode its structure onto output vectors that represent nodes in an abstract geometric space, enabl…
Community DetectionData CompressionLink PredictionSolving High-dimensional Inverse Problems Using Amortized Likelihood-free Inference with Noisy and Incomplete Data
We present a likelihood-free probabilistic inversion method based on normalizing flows for high-dimensional inverse problems. The proposed method is composed of two complementary networks: a summary network for data comp…
Data Compressionparameter estimationEfficient Compression of Sparse Accelerator Data Using Implicit Neural Representations and Importance Sampling
High-energy, large-scale particle colliders in nuclear and high-energy physics generate data at extraordinary rates, reaching up to $1$ terabyte and several petabytes per second, respectively. The development of real-tim…
Data CompressionConstruction of generalized samplets in Banach spaces
Recently, samplets have been introduced as localized discrete signed measures which are tailored to an underlying data set. Samplets exhibit vanishing moments, i.e., their measure integrals vanish for all polynomials up …
Data CompressionAIDetx: a compression-based method for identification of machine-learning generated text
This paper introduces AIDetx, a novel method for detecting machine-generated text using data compression techniques. Traditional approaches, such as deep learning classifiers, often suffer from high computational costs a…
Data CompressionHEMGS: A Hybrid Entropy Model for 3D Gaussian Splatting Data Compression
Fast progress in 3D Gaussian Splatting (3DGS) has made 3D Gaussians popular for 3D modeling and image rendering, but this creates big challenges in data storage and transmission. To obtain a highly compact 3DGS represent…
3DGSAttributeData CompressionTransCompressor: LLM-Powered Multimodal Data Compression for Smart Transportation
The incorporation of Large Language Models (LLMs) into smart transportation systems has paved the way for improving data management and operational efficiency. This study introduces TransCompressor, a novel framework tha…
Data CompressionManagementRetrievalEnabling DBSCAN for Very Large-Scale High-Dimensional Spaces
DBSCAN is one of the most important non-parametric unsupervised data analysis tools. By applying DBSCAN to a dataset, two key analytical results can be obtained: (1) clustering data points based on density distribution a…
ClusteringData CompressionVariable Rate Neural Compression for Sparse Detector Data
High-energy large-scale particle colliders generate data at extraordinary rates. Developing real-time high-throughput data compression algorithms to reduce data volume and meet the bandwidth requirement for storage has b…
Data CompressionDeep LearningBVI-CR: A Multi-View Human Dataset for Volumetric Video Compression
The advances in immersive technologies and 3D reconstruction have enabled the creation of digital replicas of real-world objects and environments with fine details. These processes generate vast amounts of 3D data, requi…
3D ReconstructionData CompressionVideo CompressionTowards efficient compression and communication for prototype-based decentralized learning
In prototype-based federated learning, the exchange of model parameters between clients and the master server is replaced by transmission of prototypes or quantized versions of the data samples to the aggregation server.…
Data CompressionFederated LearningRendering-Oriented 3D Point Cloud Attribute Compression using Sparse Tensor-based Transformer
The evolution of 3D visualization techniques has fundamentally transformed how we interact with digital content. At the forefront of this change is point cloud technology, offering an immersive experience that surpasses …
AttributeData CompressionHiHa: Introducing Hierarchical Harmonic Decomposition to Implicit Neural Compression for Atmospheric Data
The rapid development of large climate models has created the requirement of storing and transferring massive atmospheric data worldwide. Therefore, data compression is essential for meteorological research, but an effic…
Data CompressionA method based on Generative Adversarial Networks for disentangling physical and chemical properties of stars in astronomical spectra
Data compression techniques focused on information preservation have become essential in the modern era of big data. In this work, an encoder-decoder architecture has been designed, where adversarial training, a modifica…
Data Compression