The program focused on the following four themes:
- Optimization: How and why can deep models be fit to observed (training) data?
- Generalization: Why do these trained models work well on similar but unobserved (test) data?
- Robustness: How can we analyze and improve the performance of these models when applied outside their intended conditions?
- Generative methods: How can deep learning be used to model probability distributions?
J. Su, S. Maji, и B. Hariharan. (2019)cite arxiv:1910.03560Comment: ECCV 2020 camera ready. This is an updated version of "Boosting Supervision with Self-Supervision for Few-shot Learning" arXiv:1906.07079.