Summary of Wildgen: Long-horizon Trajectory Generation For Wildlife, by Ali Al-lawati et al.
WildGEN: Long-horizon Trajectory Generation for Wildlife
by Ali Al-Lawati, Elsayed Eshra, Prasenjit Mitra
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computers and Society (cs.CY)
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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 This paper presents WildGEN, a novel framework for generating movement characteristics of wild geese using Variational Auto-encoders (VAEs). The authors aim to address the challenge of obtaining additional real data in wildlife studies, which can be costly, time-consuming, and raise ethical concerns. By employing VAEs with sparse truth samples, WildGEN generates realistic trajectories that can enrich training corpora for deep learning applications and facilitate simulation tasks. The generated trajectories are post-processed using smoothing filters to reduce excessive wandering. Evaluation is conducted through visual inspection, Hausdorff distance computation, and Pearson Correlation Coefficient calculation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Wild animals like geese move around in certain ways. Scientists want to know how they move so they can learn more about them without having to collect lots of data by following the animals around. This is expensive, time-consuming, and raises ethical questions. To help solve this problem, researchers developed a new method called WildGEN. It uses special computer algorithms to create fake movement patterns that are similar to real ones. The generated movements can be used to train computers to recognize animal behavior and even simulate how animals move in different situations. Scientists checked the accuracy of the generated movements by comparing them to real ones. |
Keywords
* Artificial intelligence * Deep learning
