Yoshua Bengio Team Challenges the Task-Diversity Paradigm in Meta-Learning
Meta-learning, or learning to learn, enables machines to learn new skills or adapt to new environments rapidly with only a few training examples. Meta-learning is expected to play an important role in the development of next-generation AI models, and as such there is increasing interest in improving the performance of meta-learning algorithms.
The conventional wisdom in the machine learning research community is that meta-learning performance will improve when models are trained on more diverse tasks. A research team from Mila, Québec Artificial Intelligence Institute, Université de Montréal, CIFAR and IVADO Labs challenges this assumption in the new paper The Effect of Diversity in Meta-Learning, arguing that repeating the same tasks over the training phase can achieve performance similar to models trained on uniform sampling.
The team summarizes their main contributions as:
- We show that, against conventional wisdom, task diversity does not significantly boost performance in meta-learning. Instead, limiting task diversity and repeating the same tasks over the training phase allows the model to obtain…
