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Feature Compression

Compress data for machine interpretability to perform downstream tasks, rather than for human perception.

Papers

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Showing 76–96 of 96 papers

TitleStatusHype
Collaborative Intelligence: Challenges and Opportunities—0
Communication-Computation Efficient Device-Edge Co-Inference via AutoML—0
Compact Representation for Image Classification: To Choose or to Compress?—0
Comparing of Term Clustering Frameworks for Modular Ontology Learning—0
Complementary Bi-directional Feature Compression for Indoor 360° Semantic Segmentation with Self-distillation—0
Compressive Feature Selection for Remote Visual Multi-Task Inference—0
Cross-architecture universal feature coding via distribution alignment—0
Cross Modal Compression: Towards Human-comprehensible Semantic Compression—0
DeepAdaIn-Net: Deep Adaptive Device-Edge Collaborative Inference for Augmented Reality—0
Deep feature compression for collaborative object detection—0
Joint Device-Edge Inference over Wireless Links with Pruning—0
Deep Reinforcement Learning for Wireless Resource Allocation Using Buffer State Information—0
Derivation of Information-Theoretically Optimal Adversarial Attacks with Applications to Robust Machine Learning—0
Distilling Channels for Efficient Deep Tracking—0
Distributed and Rate-Adaptive Feature Compression—0
DMOFC: Discrimination Metric-Optimized Feature Compression—0
Dual-Domain Homogeneous Fusion with Cross-Modal Mamba and Progressive Decoder for 3D Object Detection—0
Dynamic Semantic Compression for CNN Inference in Multi-access Edge Computing: A Graph Reinforcement Learning-based Autoencoder—0
Dynamic Submodular Maximization—0
End-to-End Facial Deep Learning Feature Compression with Teacher-Student Enhancement—0
End-to-End Learnable Multi-Scale Feature Compression for VCM—0
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