Base Class Feature Space

Base class feature space research centers on effectively leveraging pre-trained models and their learned representations for improved performance on downstream tasks, particularly in few-shot and incremental learning scenarios. Current efforts focus on mitigating issues like feature space collisions between base and novel classes, often employing techniques to refine or restructure the base feature space to accommodate new data efficiently. This research is crucial for advancing machine learning capabilities in data-scarce environments and improving the robustness of models to unseen data, with applications ranging from object detection to open-set classification.