Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
BottleFit: Learning Compressed Representations in Deep Neural Networks for Effective and Efficient Split Computing
Although mission-critical applications require the use of deep neural networks (DNNs), their continuous execution at mobile devices results in a significant increase in energy consumption. While edge offloading can decre…
Data CompressionGPUimage-classificationImage ClassificationUnderstanding Entropy Coding With Asymmetric Numeral Systems (ANS): a Statistician's Perspective
Entropy coding is the backbone data compression. Novel machine-learning based compression methods often use a new entropy coder called Asymmetric Numeral Systems (ANS) [Duda et al., 2015], which provides very close to op…
BIG-bench Machine LearningData CompressionLearning Based Multi-Modality Image and Video Compression
Multi-modality (i.e., multi-sensor) data is widely used in various vision tasks for more accurate or robust perception. However, the increased data modalities bring new challenges for data storage and transmission. T…
Data CompressionVideo CompressionUniform-in-Phase-Space Data Selection with Iterative Normalizing Flows
Improvements in computational and experimental capabilities are rapidly increasing the amount of scientific data that is routinely generated. In applications that are constrained by memory and computational intensity, ex…
Data CompressionDimensionality Reductionscientific discoveryStrategies in JPEG compression using Convolutional Neural Network (CNN)
Interests in digital image processing are growing enormously in recent decades. As a result, different data compression techniques have been proposed which are concerned mostly with the minimization of information used f…
Data CompressionImage CompressionA Novel Sequential Coreset Method for Gradient Descent Algorithms
A wide range of optimization problems arising in machine learning can be solved by gradient descent algorithms, and a central question in this area is how to efficiently compress a large-scale dataset so as to reduce the…
Data CompressionLossless Compression with Probabilistic Circuits
Despite extensive progress on image generation, common deep generative model architectures are not easily applied to lossless compression. For example, VAEs suffer from a compression cost overhead due to their latent var…
Data CompressionImage GenerationTowards Empirical Sandwich Bounds on the Rate-Distortion Function
Rate-distortion (R-D) function, a key quantity in information theory, characterizes the fundamental limit of how much a data source can be compressed subject to a fidelity criterion, by any compression algorithm. As rese…
Data CompressionImage CompressionCOMET: A Novel Memory-Efficient Deep Learning Training Framework by Using Error-Bounded Lossy Compression
Training wide and deep neural networks (DNNs) require large amounts of storage resources such as memory because the intermediate activation data must be saved in the memory during forward propagation and then restored fo…
Data CompressionA new technique for compression of data sets
Data compression techniques are characterized by four key performance indices which are (i) associated accuracy, (ii) compression ratio, (iii) computational work, and (iv) degree of freedom. The method of data compressio…
Data CompressionEfficient Data Compression for 3D Sparse TPC via Bicephalous Convolutional Autoencoder
Real-time data collection and analysis in large experimental facilities present a great challenge across multiple domains, including high energy physics, nuclear physics, and cosmology. To address this, machine learning …
Data CompressionDecoderCompressing Sensor Data for Remote Assistance of Autonomous Vehicles using Deep Generative Models
In the foreseeable future, autonomous vehicles will require human assistance in situations they can not resolve on their own. In such scenarios, remote assistance from a human can provide the required input for the vehic…
Autonomous VehiclesData CompressionSSMF: Shifting Seasonal Matrix Factorization
Given taxi-ride counts information between departure and destination locations, how can we forecast their future demands? In general, given a data stream of events with seasonal patterns that innovate over time, how can …
Data CompressionGraph Wedgelets: Adaptive Data Compression on Graphs based on Binary Wedge Partitioning Trees and Geometric Wavelets
We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the partit…
Data CompressionOperationalizing Convolutional Neural Network Architectures for Prohibited Object Detection in X-Ray Imagery
The recent advancement in deep Convolutional Neural Network (CNN) has brought insight into the automation of X-ray security screening for aviation security and beyond. Here, we explore the viability of two recent end-to-…
Data AugmentationData CompressionImage Compressionobject-detection+1Data Twinning
In this work, we develop a method named Twinning, for partitioning a dataset into statistically similar twin sets. Twinning is based on SPlit, a recently proposed model-independent method for optimally splitting a datase…
Data CompressionEfficient GPU implementation of randomized SVD and its applications
Matrix decompositions are ubiquitous in machine learning, including applications in dimensionality reduction, data compression and deep learning algorithms. Typical solutions for matrix decompositions have polynomial com…
Data CompressionDeep LearningDimensionality ReductionGPUTransformer-based Transform Coding
Neural data compression based on nonlinear transform coding has made great progress over the last few years, mainly due to improvements in prior models, quantization methods and nonlinear transforms. A general trend in m…
Computational EfficiencyData CompressionImage CompressionQuantization+1Probability Distribution on Full Rooted Trees
The recursive and hierarchical structure of full rooted trees is applicable to represent statistical models in various areas, such as data compression, image processing, and machine learning. In most of these cases, the …
Data CompressionModel SelectionSparse Plus Low Rank Matrix Decomposition: A Discrete Optimization Approach
We study the Sparse Plus Low-Rank decomposition problem (SLR), which is the problem of decomposing a corrupted data matrix into a sparse matrix of perturbations plus a low-rank matrix containing the ground truth. SLR is …
Collaborative FilteringData Compression