Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Compressive dual-comb spectroscopy
Broadband, high resolution and rapid measurement of dual-comb spectroscopy (DCS) generates a large amount of data stream. We numerically demonstrate significant data compression of DCS spectra by using a compressive sens…
Compressive SensingData CompressionBETULA: Numerically Stable CF-Trees for BIRCH Clustering
BIRCH clustering is a widely known approach for clustering, that has influenced much subsequent research and commercial products. The key contribution of BIRCH is the Clustering Feature tree (CF-Tree), which is a compres…
ClusteringData CompressionA Multiparametric Class of Low-complexity Transforms for Image and Video Coding
Discrete transforms play an important role in many signal processing applications, and low-complexity alternatives for classical transforms became popular in recent years. Particularly, the discrete cosine transform (DCT…
Data CompressionImage CompressionCompressed-Domain Detection and Estimation for Colocated MIMO Radar
This paper proposes compressed domain signal processing (CSP) multiple input multiple output (MIMO) radar, a MIMO radar approach that achieves substantial sample complexity reduction by exploiting the idea of CSP. CSP MI…
compressed sensingCompressive SensingData CompressionPhysically interpretable machine learning algorithm on multidimensional non-linear fields
In an ever-increasing interest for Machine Learning (ML) and a favorable data development context, we here propose an original methodology for data-based prediction of two-dimensional physical fields. Polynomial Chaos Ex…
BIG-bench Machine LearningData CompressionDimensionality ReductionInterpretable Machine Learning+1Task-Oriented Data Compression for Multi-Agent Communications Over Bit-Budgeted Channels
Various applications for inter-machine communications are on the rise. Whether it is for autonomous driving vehicles or the internet of everything, machines are more connected than ever to improve their performance in fu…
Autonomous DrivingData CompressionQuantizationDynamic Compression Ratio Selection for Edge Inference Systems with Hard Deadlines
Implementing machine learning algorithms on Internet of things (IoT) devices has become essential for emerging applications, such as autonomous driving, environment monitoring. But the limitations of computation capabili…
Autonomous DrivingBIG-bench Machine LearningData CompressionLite Audio-Visual Speech Enhancement
Previous studies have confirmed the effectiveness of incorporating visual information into speech enhancement (SE) systems. Despite improved denoising performance, two problems may be encountered when implementing an aud…
Data CompressionDenoisingSpeech EnhancementOn compression rate of quantum autoencoders: Control design, numerical and experimental realization
Quantum autoencoders which aim at compressing quantum information in a low-dimensional latent space lie in the heart of automatic data compression in the field of quantum information. In this paper, we establish an upper…
Data CompressionThe optimal transport paradigm enables data compression in data-driven robust control
A new data-enabled control technique for uncertain linear time-invariant systems, recently conceived by Coulson et\ al., builds upon the direct optimization of controllers over input/output pairs drawn from a large datas…
Data CompressionRegularized L21-Based Semi-NonNegative Matrix Factorization
We present a general-purpose data compression algorithm, Regularized L21 Semi-NonNegative Matrix Factorization (L21 SNF). L21 SNF provides robust, parts-based compression applicable to mixed-sign data for which high fide…
BIG-bench Machine LearningData CompressionEfficient Privacy Preserving Edge Computing Framework for Image Classification
In order to extract knowledge from the large data collected by edge devices, traditional cloud based approach that requires data upload may not be feasible due to communication bandwidth limitation as well as privacy and…
ClassificationData CompressionEdge-computingFederated Learning+4Active Training of Physics-Informed Neural Networks to Aggregate and Interpolate Parametric Solutions to the Navier-Stokes Equations
The goal of this work is to train a neural network which approximates solutions to the Navier-Stokes equations across a region of parameter space, in which the parameters define physical properties such as domain shape a…
Active LearningData CompressionMedical DiagnosisNeural network compression via learnable wavelet transforms
Wavelets are well known for data compression, yet have rarely been applied to the compression of neural networks. This paper shows how the fast wavelet transform can be used to compress linear layers in neural networks. …
Data CompressionNeural Network CompressionEdge Computing For Smart Health: Context-aware Approaches, Opportunities, and Challenges
Improving efficiency of healthcare systems is a top national interest worldwide. However, the need of delivering scalable healthcare services to the patients while reducing costs is a challenging issue. Among the most pr…
Data CompressionEdge-computingEvent DetectionCharacterizing Generalized Rate-Distortion Performance of Video Coding: An Eigen Analysis Approach
Rate-distortion (RD) theory is at the heart of lossy data compression. Here we aim to model the generalized RD (GRD) trade-off between the visual quality of a compressed video and its encoding profiles (e.g., bitrate and…
Data CompressionFeedback Recurrent Autoencoder for Video Compression
Recent advances in deep generative modeling have enabled efficient modeling of high dimensional data distributions and opened up a new horizon for solving data compression problems. Specifically, autoencoder based learne…
Data CompressionMS-SSIMSSIMVideo CompressionDetecting Deepfakes with Metric Learning
With the arrival of several face-swapping applications such as FaceApp, SnapChat, MixBooth, FaceBlender and many more, the authenticity of digital media content is hanging on a very loose thread. On social media platform…
Data CompressionFace SwappingGeneral ClassificationMetric Learning+1Recovering compressed images for automatic crack segmentation using generative models
In a structural health monitoring (SHM) system that uses digital cameras to monitor cracks of structural surfaces, techniques for reliable and effective data compression are essential to ensure a stable and energy effici…
Compressive SensingCrack SegmentationData CompressionStructural Health MonitoringFine-grain atlases of functional modes for fMRI analysis
Population imaging markedly increased the size of functional-imaging datasets, shedding new light on the neural basis of inter-individual differences. Analyzing these large data entails new scalability challenges, comput…
Data Compression