Feature Compression
12 papers with code • 0 benchmarks • 0 datasets
Compress data for machine interpretability to perform downstream tasks, rather than for human perception.
Benchmarks
These leaderboards are used to track progress in Feature Compression
Most implemented papers
Supervised Compression for Resource-Constrained Edge Computing Systems
yoshitomo-matsubara/supervised-compression
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There has been much interest in deploying deep learning algorithms on low-powered devices, including smartphones, drones, and medical sensors.
Context-aware Deep Feature Compression for High-speed Visual Tracking
We propose a new context-aware correlation filter based tracking framework to achieve both high computational speed and state-of-the-art performance among real-time trackers.
BottleNet++: An End-to-End Approach for Feature Compression in Device-Edge Co-Inference Systems
shaojiawei07/BottleNetPlusPlus
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By exploiting the strong sparsity and the fault-tolerant property of the intermediate feature in a deep neural network (DNN), BottleNet++ achieves a much higher compression ratio than existing methods.
Lossy Compression for Lossless Prediction
YannDubs/lossyless
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NeurIPS 2021
Most data is automatically collected and only ever "seen" by algorithms.
Context-Aware Compilation of DNN Training Pipelines across Edge and Cloud
dixiyao/Context-Aware-Compilation-of-DNN-Training-Pipelines-across-Edge-and-Cloud
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Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies 2021
Experimental results show that our system not only adapts well to, but also draws on the varying contexts, delivering a practical and efficient solution to edge-cloud model training.
SC2: Supervised Compression for Split Computing
yoshitomo-matsubara/sc2-benchmark
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Split computing distributes the execution of a neural network (e. g., for a classification task) between a mobile device and a more powerful edge server.
Multi-Agent Collaborative Inference via DNN Decoupling: Intermediate Feature Compression and Edge Learning
Hao840/MAHPPO
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In this paper, we study the multi-agent collaborative inference scenario, where a single edge server coordinates the inference of multiple UEs.
Compressing Features for Learning with Noisy Labels
yingyichen-cyy/Nested-Co-teaching
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This decomposition provides three insights: (i) it shows that over-fitting is indeed an issue for learning with noisy labels; (ii) through an information bottleneck formulation, it explains why the proposed feature compression helps in combating label noise; (iii) it gives explanations on the performance boost brought by incorporating compression regularization into Co-teaching.
Supervised Feature Compression based on Counterfactual Analysis
Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning.
Efficient Feature Compression for Edge-Cloud Systems
duanzhiihao/edge-cloud-rac
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Optimizing computation in an edge-cloud system is an important yet challenging problem.
