Few-shot learning with adaptively initialized task optimizer: a practical meta-learning approach

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Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks (2022, background): “MAML is widely applied into vast learning scenarios, e.g., robotics con-trol [49], neural translation [50] and uncertainty estimation [51], and many improved algorithms [52], [53], [54], [55], [56] have been made to further boost its performance.”
Deep Class-Incremental Learning: A Survey (2023, background): “, few-shot [204], imbalanced [136], with noisy label [205], weak supervised [206], multi modal [207], concept drift [13], novel classes [208], [209] etc.”
How to Train Your MAML to Excel in Few-Shot Classification (2021, background): “Recent years have witnessed an abundance of new approaches on meta-learning [29, 30, 48, 61, 63, 64, 68, 77, 79, 80, 94, 98], developed in various areas including few-shot learning [70, 85, 89, 105, 107, 110, 111, 115], optimization [3, 9, 53, 108], reinforcement and imitation learning [23, 24, 31, ”