because you need a lot of negatives because moko decouples the negatives from a bad size it doesn't need as much compute as sim cleared us and similar to moko they documentation invariance may be very specific to image recognition so in terms of future work left for sauce provision the gap between some supervised learning and supervised learning is to not close if you consider just the same amount of compute training time and the same candidate augmentations use so and also fine-tuning to downstream tasks the