Deep Bayesian Active Learning for Multiple Correct Outputs

Understand

Typical active learning strategies are designed for tasks, such as classification, with the assumption that the output space is mutually exclusive.

• The assumption that these tasks always have exactly one correct answer has resulted in the creation of numerous uncertainty-based measurements, such as entropy and least confidence, which operate over a model's outputs.
• Unfortunately, many real-world vision tasks, like visual question answering and image captioning, have multiple correct answers, causing these measurements to overestimate uncertainty and sometimes perform worse than a random sampling baseline.
• In this paper, we propose a new paradigm that estimates uncertainty in the model's internal hidden space instead of the model's output space.