The main objective of this project is to investigate, develop and evaluate state-of-the-art deep-learning algorithms for joint few-shot classification and out-of-distribution (OOD) detection. Few-shot learning deals with the challenges of limited supervision, and OOD detection attempts to identify inputs that do not belong to the set of classes seen during training. The two research problems […]
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