[Few-shot learning][2.1] transfer learning
Channel: Max Patacchiola
Date: 2020-01-20

In this episode a more detailed introduction to the Transfer Learning approach for Few-shot learning. In particular the Baseline and Baseline++ methods described in Chen et al. (2019).

The Baseline condition is a form of standard transfer learning, where the last layer of the network is fine-tuned on a set of new data points at test time. Baseline++ introduces a similarity measure in the last layer, and it perform a comparison between the latent vector and a set of prototypes. Both approaches are very easy to implement and surprisingly competitive in many settings.

References:

Chen, W.-Y., Liu, Y.-C., Kira, Z., Wang, Y.-C., and Huang,J.-B. A closer look at few-shot classification. InInternational Conference on Learning Representations, 2019.

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