Summary of Reinforcement Learning As a Parsimonious Alternative to Prediction Cascades: a Case Study on Image Segmentation, by Bharat Srikishan et al.
Reinforcement Learning as a Parsimonious Alternative to Prediction Cascades: A Case Study on Image Segmentation
by Bharat Srikishan, Anika Tabassum, Srikanth Allu, Ramakrishnan Kannan, Nikhil Muralidhar
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed PaSeR (Parsimonious Segmentation with Reinforcement Learning) learning pipeline is a non-cascading, cost-aware approach that achieves better accuracy while minimizing computational cost compared to cascaded models. Building upon the success of deep learning architectures in computer vision tasks like object detection and image segmentation, PaSeR addresses the limitation of large architectures requiring significant resources for inference. By introducing a new metric IoU/GigaFlop, the authors demonstrate PaSeR’s adaptability and performance improvement on real-world and standard datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PaSeR is a new way to do computer learning that makes it more efficient while still being very accurate. It’s designed to work well even when there isn’t much computing power available. The researchers tested it on some real-world data and showed that it did better than other methods, especially when the data was noisy. |
Keywords
* Artificial intelligence * Deep learning * Image segmentation * Inference * Object detection * Reinforcement learning
