A Partially-Supervised Reinforcement Learning Framework for Visual Active Search
Authors: Anindya Sarkar, Nathan Jacobs, Yevgeniy Vorobeychik
NeurIPS 2023 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experiments demonstrate that the proposed representation and meta-learning frameworks significantly outperform state of the art in visual active search on several problem domains. |
| Researcher Affiliation | Academia | Anindya Sarkar Nathan Jacobs Yevgeniy Vorobeychik EMAIL, Department of Computer Science and Engineering Washington University in St. Louis |
| Pseudocode | Yes | Algorithm 1 The PSVAS algorithm. |
| Open Source Code | Yes | Our code is publicly available at this link. |
| Open Datasets | Yes | We evaluate the proposed approach using two datasets: x View [16] and DOTA [17]. |
| Dataset Splits | Yes | We use 67% and 33% of the large satellite images to train and test the policy network respectively. |
| Hardware Specification | Yes | We use 1 NVidia A100 and 3 Ge Force GTX 1080Ti GPU servers for all our experiments. |
| Software Dependencies | No | The paper mentions software components like 'Adam optimizer' and 'Res Net-34' but does not provide specific version numbers for programming languages or libraries such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We use a learning rate of 10 4, batch size of 16, number of training epochs 200, and the Adam optimizer to train the policy network in all experimental settings. |