Yin Yang Convolutional Nets: Image Manifold Extraction by the Analysis
  of Opposites
Augusto Seben da Rosa Frederico Santos de Oliveira Anderson da Silva Soares Arnaldo Candido Junior
Computer vision in general presented several advances such as training
optimizations, new architectures (pure attention, efficient block, vision
language models, generative models, among others). This have improved
performance in several tasks such as classification, and others. However, the
majority of these models focus on modifications that are taking distance from
realistic neuroscientific approaches related to the brain. In this work, we
adopt a more bio-inspired approach and present the Yin Yang Convolutional
Network, an architecture that extracts visual manifold, its blocks are intended
to separate analysis of colors and forms at its initial layers, simulating
occipital lobe's operations. Our results shows that our architecture provides
State-of-the-Art efficiency among low parameter architectures in the dataset
CIFAR-10. Our first model reached 93.32\% test accuracy, 0.8\% more than the
older SOTA in this category, while having 150k less parameters (726k in total).
Our second model uses 52k parameters, losing only 3.86\% test accuracy. We also
performed an analysis on ImageNet, where we reached 66.49\% validation accuracy
with 1.6M parameters. We make the code publicly available at:
https://github.com/NoSavedDATA/YinYang_CNN.