> What I don't understand is how they do the composition Convolutional filters lend themselves to rich combinatorics of compositions[1]: think of them as of co

> What I don't understand is how they do the composition Convolutional filters lend themselves to rich combinatorics of compositions[1]: think of them as of context-dependent texture-atoms, repulsing and attracting over the variations of the local multi-dimensional context in the image. The composition is literally a convolutional transformation of local channels encoding related principal components of context. Astronomical amounts of computations spent via training allow the network to form a lego-set of these texture-atoms in a general distribution of contexts. At least this is my intuition for the nature of the convnets. 1. https://microscope.openai.com/models/contrastive_16x/image_block_4_0_Add_8_0?models.op.feature_vis.type=channel&models.op.technique=feature_vis https://microscope.openai.com/models/contrastive_16x/image_b...