12 papers with code • 0 benchmarks • 0 datasets
Compress data for machine interpretability to perform downstream tasks, rather than for human perception.
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Most implemented papers
Supervised 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.
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
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
Most data is automatically collected and only ever "seen" by algorithms.
Context-Aware Compilation of DNN Training Pipelines across Edge and Cloud
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
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
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
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
Optimizing computation in an edge-cloud system is an important yet challenging problem.