Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Fundamental Limits of Two-layer Autoencoders, and Achieving Them with Gradient Methods
Autoencoders are a popular model in many branches of machine learning and lossy data compression. However, their fundamental limits, the performance of gradient methods and the features learnt during optimization remain …
Data CompressionScalable Hybrid Learning Techniques for Scientific Data Compression
Data compression is becoming critical for storing scientific data because many scientific applications need to store large amounts of data and post process this data for scientific discovery. Unlike image and video compr…
Data CompressionGPUscientific discoveryVideo CompressionGeneFormer: Learned Gene Compression using Transformer-based Context Modeling
With the development of gene sequencing technology, an explosive growth of gene data has been witnessed. And the storage of gene data has become an important issue. Traditional gene data compression methods rely on gener…
Data CompressionTowards Efficient and Accurate Approximation: Tensor Decomposition Based on Randomized Block Krylov Iteration
Efficient and accurate low-rank approximation (LRA) methods are of great significance for large-scale data analysis. Randomized tensor decompositions have emerged as powerful tools to meet this need, but most existing me…
Data CompressionDenoisingTensor DecompositionCNeRV: Content-adaptive Neural Representation for Visual Data
Compression and reconstruction of visual data have been widely studied in the computer vision community, even before the popularization of deep learning. More recently, some have used deep learning to improve or refine e…
Data CompressionDecoderDynamic Predictive Sampling Analog to Digital Converter for Sparse Signal Sensing
This paper presents a dynamic predictive sampling (DPS) based analog-to-digital converter (ADC) that provides a non-uniform sampling of input analog continuous-time signals. The processing unit generates a dynamic predic…
Data CompressionQuantizationFully Digital Second-order Level-crossing Sampling ADC for Data Saving in Sensing Sparse Signals
This paper presents a fully integrated second-order level-crossing sampling data converter for real-time data compression and feature extraction. Compared with level-sampling ADCs which sample at fixed voltage levels, th…
Data CompressionQuantizationSqueeze flow of micro-droplets: convolutional neural network with trainable and tunable refinement
We propose a platform based on neural networks to solve the image-to-image translation problem in the context of squeeze flow of micro-droplets. In the first part of this paper, we present the governing partial different…
Data CompressionImage-to-Image TranslationTranslationTINC: Tree-structured Implicit Neural Compression
Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is …
Data CompressionGoal-Oriented Communications for the IoT and Application to Data Compression
Internet of Things (IoT) devices will play an important role in emerging applications, since their sensing, actuation, processing, and wireless communication capabilities stimulate data collection, transmission and decis…
Data CompressionQuadConv: Quadrature-Based Convolutions with Applications to Non-Uniform PDE Data Compression
We present a new convolution layer for deep learning architectures which we call QuadConv -- an approximation to continuous convolution via quadrature. Our operator is developed explicitly for use on non-uniform, mesh-ba…
Data CompressionEfficient Compressed Ratio Estimation Using Online Sequential Learning for Edge Computing
Owing to the widespread adoption of the Internet of Things, a vast amount of sensor information is being acquired in real time. Accordingly, the communication cost of data from edge devices is increasing. Compressed sens…
compressed sensingData CompressionEdge-computingreinforcement-learning+1Spiking sampling network for image sparse representation and dynamic vision sensor data compression
Sparse representation has attracted great attention because it can greatly save storage re- sources and find representative features of data in a low-dimensional space. As a result, it may be widely applied in engineerin…
compressed sensingData CompressionDenoisingDictionary Learning+1Optimal Compression for Minimizing Classification Error Probability: an Information-Theoretic Approach
We formulate the problem of performing optimal data compression under the constraints that compressed data can be used for accurate classification in machine learning. We show that this translates to a problem of minimiz…
ClassificationData CompressionA Novel Approach for Neuromorphic Vision Data Compression based on Deep Belief Network
A neuromorphic camera is an image sensor that emulates the human eyes capturing only changes in local brightness levels. They are widely known as event cameras, silicon retinas or dynamic vision sensors (DVS). DVS record…
Data CompressionMulti-rate adaptive transform coding for video compression
Contemporary lossy image and video coding standards rely on transform coding, the process through which pixels are mapped to an alternative representation to facilitate efficient data compression. Despite impressive perf…
Data CompressionQuantizationVideo CompressionAre Deep Sequence Classifiers Good at Non-Trivial Generalization?
Recent advances in deep learning models for sequence classification have greatly improved their classification accuracy, specially when large training sets are available. However, several works have suggested that under …
ClassificationData CompressionDeep LearningCompressive Sensing of ECG Signals using Plug-and-Play Regularization
Compressive Sensing (CS) has recently attracted attention for ECG data compression. In CS, an ECG signal is projected onto a small set of random vectors. Recovering the original signal from such compressed measurements r…
Compressive SensingData CompressionGOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression
Neural-network-based approaches recently emerged in the field of data compression and have already led to significant progress in image compression, especially in achieving a higher compression ratio. In the lossless ima…
ClusteringData CompressionImage CompressionHeartSpot: Privatized and Explainable Data Compression for Cardiomegaly Detection
Advances in data-driven deep learning for chest X-ray image analysis underscore the need for explainability, privacy, large datasets and significant computational resources. We frame privacy and explainability as a lossy…
Data CompressionImage Compression