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Charting the Right Manifold: Manifold Mixup for Few-shot Learning
[article]
2020
arXiv
pre-print
Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution. Since the goal of few-shot learning is closely linked to robust representation learning, we study Manifold Mixup in this problem setting. Self-supervised learning is another technique that learns
arXiv:1907.12087v4
fatcat:a5v3zqnvyva3zar62g6as44u3a