Seeded segmentation in computer vision and graph-based semi-supervised machine learning are essentially thesameproblem.

A recent trend in online algorithms has seen the use of future predictions generated by ML techniques to bypass pessimistic worst-case lower bounds.

A naiveimplementation of this approach leads to the dynamic component taking over the static one as the representation of the former is inherently more general and prone to overfitting.

A risk measure often focuses on the worst returns out of the agent'sexperience.

Deep networks have shown remarkable results in the task of object detection. However, their performance suffers critical drops when they are subsequently trained on novel classes without any sample from the base classes originally used to train the model. This phenomenon is known as catastrophic forgetting. Recently, several incremental learning methods are proposed to mitigate catastrophic forgetting for object detection. Despite the effectiveness, these methods require co-occurrence of the unlabeled base classes in the training data of the novelclasses. This requirement isimpractical in manyreal-world settings since the base classes do not necessarily co-occur with the novel classes.

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