Self-Supervised Interpretable End-to-End Learning via Latent Functional Modularity
Authors: Hyunki Seong, Hyunchul Shim
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In realworld indoor environments, Mo Net demonstrates effective visual autonomous navigation, outperforming baseline models by 7% to 28% in task specificity analysis. |
| Researcher Affiliation | Academia | 1School of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, South Korea. Correspondence to: Hyunki Seong <hynkis@kaist.ac.kr>. |
| Pseudocode | No | The paper includes equations and diagrams but does not feature any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | A demo video, codes, and dataset for quantitative results and interpretation are available at https://sites.google.com/view/monet-lgc. |
| Open Datasets | Yes | A demo video, codes, and dataset for quantitative results and interpretation are available at https://sites.google.com/view/monet-lgc. |
| Dataset Splits | Yes | The data is split into training and validation sets at a ratio of 80 : 20. |
| Hardware Specification | Yes | Our platform consists of a 1/10 scale racing car chassis (TT-02) equipped with an embedded computer (Jetson Xavier NX) and a controller (Arduino). |
| Software Dependencies | No | The paper mentions using "Adam optimizer" but does not specify a version number for the optimizer or any other software libraries/frameworks. |
| Experiment Setup | Yes | Batch size 512 Total training iterations 650k Optimizer Adam Similarity factor κ 0.5 Weight for the LGC loss term λLGC 5e-4 Learning rate 3e-4 Learning rate scheduler Lambda LR Scheduler factor 3e-4 |