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Deep Learning

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Deep Learning

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Research Frontiers in Few-Shot and Zero-Shot Learning

Development of deep learning models capable of learning from extremely limited labeled data or recognizing unseen classes.

Semantic Bridging in Zero-Shot Classification Spaces

Meta-Learning Dynamics Across Task Distributions

Transductive Inference Without Labeled Support Sets

Cross-Modal Knowledge Transfer in Few-Shot Settings

Prototype Geometry and Decision Boundary Collapse

Continual Few-Shot Learning Without Catastrophic Forgetting

Adversarial Robustness in Low-Data Regimes

Compositional Generalization via Attribute-Based Zero-Shot Learning

Neural Architecture Search for Sample-Efficient Learning

Implicit Bias of Gradient Descent in Few-Shot Adaptation

All Deep Learning PhD categories