Summary of Self-supervised Interpretable End-to-end Learning Via Latent Functional Modularity, by Hyunki Seong et al.
Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
by Hyunki Seong, David Hyunchul Shim
First submitted to arxiv on: 21 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO)
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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 MoNet model is a novel, functionally modular network designed for self-supervised and interpretable end-to-end learning. It leverages its modularity with a latent-guided contrastive loss function to learn task-specific decision-making processes without requiring task-level supervision. The method also incorporates an online, post-hoc explainability approach that enhances the interpretability of end-to-end inferences while maintaining sensorimotor control performance. MoNet demonstrates effective visual autonomous navigation in real-world indoor environments, outperforming baseline models by 7% to 28% in task specificity analysis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MoNet is a new way for robots and computers to learn without being told what to do specifically. It’s like a map that helps them make decisions based on what they see and experience. This model can even explain its own decisions, which is important for things like self-driving cars or robots that need to understand what they’re seeing. The results show that MoNet works well in real-world situations, like navigating a room. |
Keywords
* Artificial intelligence * Contrastive loss * Self supervised
