One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point.
Recently, there has been an increasing interest in automated prompt optimization based on reinforcement learning (RL).
To achieve a better stability-plasticity trade-off, we propose Backward Feature Projection (BFP), a method for continual learning that allows the new features to change up to a learnable linear transformation of the old features.
While the common method for evaluating CIL algorithms is based on average test accuracy for all learned classes, we argue that maximizing accuracy alone does not necessarily lead to effective CIL algorithms.
Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification.
Recent years have seen the introduction of a range of methods for post-hoc explainability of image classifier predictions.
As image-based deep learning becomes pervasive on every device, from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption.