Realistic Evaluation of Transductive Few-Shot Learning - Supplementary Material

In the main tables of the paper, we did not include the performances of α-TIM in the standard balanced setting. Here, we emphasize that α-TIM is a generalization of TIM [1] as when α 1 (i.e., the α-entropies tend to the Shannon entropies), α-TIM tends to TIM. Therefore, in the standard setting, where optimal hyper-parameter αis obtained over validation tasks that are balanced (as in the standard validation tasks of the original TIM and the other existing methods), the performance of α-TIM is the same as TIM. When αis tuned on balanced validation tasks, we obtain an optimal value of αvery close to 1, and our α-mutual information approaches the standard mutual information. When the validation tasks are uniformly random, as in our new setting and in the validation plots we provided in the main figure, one can see that the performance of α-TIM remains competitive when we tend to balanced testing tasks (i.e., when a is increasing), but is significantly better than TIM when we tend to uniformly-random testing tasks (a = 1).