A Preliminary Study on the Feature Representations of Transfer Learning and Gradient-Based Meta-Learning Techniques

Meta-learning has received considerable attention as one approach to enable deep neural networks to learn from a few data. Recent results suggest that simply fine-tuning a pre-trained network may be more effective at learning new image classification tasks from limited data than more complicated meta-learning techniques such as MAML. This is surprising as the learning behavior of MAML mimics that of fine-tuning. We investigate this phenomenon and show that the pre-trained features are more diverse and discriminative than those learned by MAML and Reptile, which specialize for adaptation in low-data regimes of a similar data distribution as the one used for training. Due to this specialization and lack of diversity, MAML and Reptile may fail to generalize to out-of-distribution tasks whereas fine-tuning can fall back on the diversity of the learned features.