Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Text Ranking and Classification using Data Compression
A well-known but rarely used approach to text categorization uses conditional entropy estimates computed using data compression tools. Text affinity scores derived from compressed sizes can be used for classification and…
ClassificationData CompressionText CategorizationA color temperature-based high-speed decolorization: an empirical approach for tone mapping applications
Grayscale images are fundamental to many image processing applications like data compression, feature extraction, printing and tone mapping. However, some image information is lost when converting from color to grayscale…
Data CompressionSSIMTone MappingMulti-dimensional graph fractional Fourier transform and its application
Many multi-dimensional signals appear in the real world, such as digital images and data that has spatial and temporal dimensions. How to show the spectrum of these multi-dimensional signals correctly is a key challenge …
Data CompressionLearned Image Coding for Machines: A Content-Adaptive Approach
Today, according to the Cisco Annual Internet Report (2018-2023), the fastest-growing category of Internet traffic is machine-to-machine communication. In particular, machine-to-machine communication of images and videos…
Data CompressionImage CompressionSupervised Compression for Resource-Constrained Edge Computing Systems
There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors. However, full-scale deep neural networks are often too resource-intensive in …
Data CompressionEdge-computingFeature CompressionImage Compression+1Communication-Efficient Federated Learning via Predictive Coding
Federated learning can enable remote workers to collaboratively train a shared machine learning model while allowing training data to be kept locally. In the use case of wireless mobile devices, the communication overhea…
Data CompressionFederated LearningQuantizationA Statistical Extension of Byte-Pair Encoding
Sub-word segmentation is currently a standard tool for training neural machine translation (MT) systems and other NLP tasks. The goal is to split words (both in the source and target languages) into smaller units which t…
Data CompressionMachine TranslationTranslationSamplets: A new paradigm for data compression
In this article, we introduce the concept of samplets by transferring the construction of Tausch-White wavelets to the realm of data. This way we obtain a multilevel representation of discrete data which directly enables…
Data CompressionCascade Decoders-Based Autoencoders for Image Reconstruction
Autoencoders are composed of coding and decoding units, hence they hold the inherent potential of high-performance data compression and signal compressed sensing. The main disadvantages of current autoencoders comprise t…
compressed sensingData CompressionImage CompressionImage ReconstructionQuantum Data Compression and Quantum Cross Entropy
The emerging field of quantum machine learning has the potential of revolutionizing our perspectives of quantum computing and artificial intelligence. In the predominantly empirical realm of quantum machine learning, a t…
BIG-bench Machine LearningData CompressionQuantum Machine LearningADVERT: An Adaptive and Data-Driven Attention Enhancement Mechanism for Phishing Prevention
Attacks exploiting the innate and the acquired vulnerabilities of human users have posed severe threats to cybersecurity. This work proposes ADVERT, a human-technical solution that generates adaptive visual aids in real-…
Data CompressionReduNet: A White-box Deep Network from the Principle of Maximizing Rate Reduction
This work attempts to provide a plausible theoretical framework that aims to interpret modern deep (convolutional) networks from the principles of data compression and discriminative representation. We argue that for hig…
AllData CompressionPrivacy Amplification Via Bernoulli Sampling
Balancing privacy and accuracy is a major challenge in designing differentially private machine learning algorithms. One way to improve this tradeoff for free is to leverage the noise in common data operations that alrea…
Bayesian InferenceData CompressionTencent Video Dataset (TVD): A Video Dataset for Learning-based Visual Data Compression and Analysis
Learning-based visual data compression and analysis have attracted great interest from both academia and industry recently. More training as well as testing datasets, especially good quality video datasets are highly des…
4kData Compressionobject-detectionObject DetectionA reconfigurable neural network ASIC for detector front-end data compression at the HL-LHC
Despite advances in the programmable logic capabilities of modern trigger systems, a significant bottleneck remains in the amount of data to be transported from the detector to off-detector logic where trigger decisions …
Data CompressionHigh-Level SynthesisQuantizationBoosting Co-teaching with Compression Regularization for Label Noise
In this paper, we study the problem of learning image classification models in the presence of label noise. We revisit a simple compression regularization named Nested Dropout. We find that Nested Dropout, though origina…
Data Compressionimage-classificationImage ClassificationLearning with noisy labels+1Efficient Sparse Coding using Hierarchical Riemannian Pursuit
Sparse coding is a class of unsupervised methods for learning a sparse representation of the input data in the form of a linear combination of a dictionary and a sparse code. This learning framework has led to state-of-t…
Data CompressionMicroshift: An Efficient Image Compression Algorithm for Hardware
In this paper, we propose an image compression algorithm called Microshift. We employ an algorithm hardware co-design methodology, yielding a hardware-friendly compression approach with low power consumption. In our meth…
Data CompressionImage CompressionBayesian subset selection and variable importance for interpretable prediction and classification
Subset selection is a valuable tool for interpretable learning, scientific discovery, and data compression. However, classical subset selection is often avoided due to selection instability, lack of regularization, and d…
Data CompressionGeneral Classificationscientific discoveryUncertainty Quantification+1Turning Channel Noise into an Accelerator for Over-the-Air Principal Component Analysis
Recently years, the attempts on distilling mobile data into useful knowledge has been led to the deployment of machine learning algorithms at the network edge. Principal component analysis (PCA) is a classic technique fo…
Data Compression