Appendix: Memory Efficient Meta-Learning with Large Images A Applying LITE to meta-learners

FiLM parameter generator so that the feature extractor can be configured for the task. The meta-testing flow is similar, with the exception of the loss computation.Figure A.1: CNAP Euclidean distance from each query set image embedding to each of the class prototypes is computed. The predicted class is the one with the minimum distance. Figure B.3: (Left) A FiLM layer operating on convolutional feature maps indexed by channel FiLM layer added to the feature extractor. Each task is composed of clips sampled from a single user's objects (random We then adapt the trained model to a task by using the task's support clips to: i) perform a These were chosen based on the number of learnable parameters in each model.