Bag of Tricks for Training Brain-Like Deep Neural Networks

Alexander Riedel

Abstract:

The human-level performance of artificial neural networks (ANNs) in visual processing has made them a much-used research subject for understanding how the visual cortex really works. To assess how well various types of ANNs represent regions of the visual cortex, the Brain-Score platform provides several standardized benchmarks. These include the measure of explained variance in ventral stream regions V1, V2, V4, IT and the object recognition behavior in primates.
The aim of this work is to find a training procedure that maximizes an ANNs average score in the Brain-Score benchmark. The proposed pipeline combines a customized version of CutMix, heavy use of image augmentations, adversarial robust training, fixing the train-test resolution discrepancy, and weight averaging.
Due to its widespread use, memory and computational efficiency, and object recognition performance, the EfficientNet-B1 architecture was used prototypically. The proposed training methods improve the public object recognition behavior metric score by 9% and the explained V1 variance by 62%, resulting in the best performing models in the Brain-Score competition 2022 . This is a strong indicator that finding the right training strategy might be crucial for developing brain-like ANNs.