In this work, we argue for the importance of an online evaluation budget for a reliable comparison of deep offline RL algorithms. First, we delineate that the online evaluation budget is problem-dependent, where some problems allow for less but …
Large-scale datasets are essential for the success of deep learning in image retrieval. However, manual assessment errors and semi-supervised annotation techniques can lead to label noise even in popular datasets. As previous works primarily studied …
Prompt engineering can be successfully used for deep offline reinforcement learning in environments that are not naturally suited for the textual representation.
In recent years, deep metric learning and its probabilistic extensions achieved state-of-the-art results in a face verification task. However, despite improvements in face verification, probabilistic methods received little attention in the …