Papers Data Compression
“Data Compression” 태그가 달린 논문 459편 · 필터 해제
Scanpath Prediction in Panoramic Videos via Expected Code Length Minimization
Predicting human scanpaths when exploring panoramic videos is a challenging task due to the spherical geometry and the multimodality of the input, and the inherent uncertainty and diversity of the output. Most previous m…
Data CompressionDiversityImitation LearningScanpath predictionRepresentation Learning via Manifold Flattening and Reconstruction
This work proposes an algorithm for explicitly constructing a pair of neural networks that linearize and reconstruct an embedded submanifold, from finite samples of this manifold. Our such-generated neural networks, call…
Data CompressionRepresentation LearningAdvanced Medical Image Representation for Efficient Processing and Transfer in Multisite Clouds
An important topic in medical research is the process of improving the images obtained from medical devices. As a consequence, there is also a need to improve medical image resolution and analysis. Another issue in this …
Data CompressionAccelerate Support Vector Clustering via Spectrum-Preserving Data Compression
This paper proposes a novel framework for accelerating support vector clustering. The proposed method first computes much smaller compressed data sets while preserving the key cluster properties of the original data sets…
ClusteringData CompressionCrossword: A Semantic Approach to Data Compression via Masking
The traditional methods for data compression are typically based on the symbol-level statistics, with the information source modeled as a long sequence of i.i.d. random variables or a stochastic process, thus establishin…
Data CompressionDecoderSnakeVoxFormer: Transformer-based Single Image\\Voxel Reconstruction with Run Length Encoding
Deep learning-based 3D object reconstruction has achieved unprecedented results. Among those, the transformer deep neural model showed outstanding performance in many applications of computer vision. We introduce SnakeVo…
3D Object ReconstructionData CompressionObject ReconstructionEnergy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures
Executing machine learning inference tasks on resource-constrained edge devices requires careful hardware-software co-design optimizations. Recent examples have shown how transformer-based deep neural network models such…
Data CompressionModel OptimizationCognitive Semantic Communication Systems Driven by Knowledge Graph: Principle, Implementation, and Performance Evaluation
Semantic communication is envisioned as a promising technique to break through the Shannon limit. However, semantic inference and semantic error correction have not been well studied. Moreover, error correction methods o…
Data CompressionSemantic CommunicationFast and Multi-aspect Mining of Complex Time-stamped Event Streams
Given a huge, online stream of time-evolving events with multiple attributes, such as online shopping logs: (item, price, brand, time), and local mobility activities: (pick-up and drop-off locations, time), how can we su…
Anomaly DetectionData CompressionJoint Task and Data Oriented Semantic Communications: A Deep Separate Source-channel Coding Scheme
Semantic communications are expected to accomplish various semantic tasks with relatively less spectrum resource by exploiting the semantic feature of source data. To simultaneously serve both the data transmission and s…
Bayesian InferenceData CompressionFast Trajectory End-Point Prediction with Event Cameras for Reactive Robot Control
Prediction skills can be crucial for the success of tasks where robots have limited time to act or joints actuation power. In such a scenario, a vision system with a fixed, possibly too low, sampling rate could lead to t…
Data CompressionCluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory
Rate-distortion theory-based outlier detection builds upon the rationale that a good data compression will encode outliers with unique symbols. Based on this rationale, we propose Cluster Purging, which is an extension o…
ClusteringData CompressionOutlier DetectionFrankenSplit: Efficient Neural Feature Compression with Shallow Variational Bottleneck Injection for Mobile Edge Computing
The rise of mobile AI accelerators allows latency-sensitive applications to execute lightweight Deep Neural Networks (DNNs) on the client side. However, critical applications require powerful models that edge devices can…
Data CompressionEdge-computingFeature CompressionImage Classification+3Breaking the Communication-Privacy-Accuracy Tradeoff with $f$-Differential Privacy
We consider a federated data analytics problem in which a server coordinates the collaborative data analysis of multiple users with privacy concerns and limited communication capability. The commonly adopted compression …
Data CompressionFederated LearningTrading Information between Latents in Hierarchical Variational Autoencoders
Variational Autoencoders (VAEs) were originally motivated (Kingma & Welling, 2014) as probabilistic generative models in which one performs approximate Bayesian inference. The proposal of $\beta$-VAEs (Higgins et al., 20…
Bayesian InferenceData CompressionRepresentation LearningGraph-Based Compensated Wavelet Lifting for Scalable Lossless Coding of Dynamic Medical Data
Lossless compression of dynamic 2D+t and 3D+t medical data is challenging regarding the huge amount of data, the characteristics of the inherent noise, and the high bit depth. Beyond that, a scalable representation is of…
Data CompressionMotion CompensationLDMIC: Learning-based Distributed Multi-view Image Coding
Multi-view image compression plays a critical role in 3D-related applications. Existing methods adopt a predictive coding architecture, which requires joint encoding to compress the corresponding disparity as well as res…
Data CompressionDecoderImage CompressionModality-Agnostic Variational Compression of Implicit Neural Representations
We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR). Bridging the gap between latent coding and sparsity, we obtai…
Data CompressionAn Energy-Efficient Reconfigurable Autoencoder Implementation on FPGA
Autoencoders are unsupervised neural networks that are used to process and compress input data and then reconstruct the data back to the original data size. This allows autoencoders to be used for different processing ap…
Data CompressionGPUimage-classificationImage ClassificationGraph-based compensated wavelet lifting for 3-D+t medical CT data
An efficient scalable data representation is an important task especially in the medical area, e.g. for volumes from Computed Tomography (CT) or Magnetic Resonance Tomography (MRT), when a downscaled version of the origi…
Computed Tomography (CT)Data Compressiongraph constructionMotion Compensation