1 code implementation • ICLR 2022 • Michelle Miller, SueYeon Chung, Kenneth D. Miller
In conclusion, divisive normalization enhances image recognition performance, most strongly when combined with canonical normalization, and in doing so it reduces manifold capacity and sparsity in early layers while increasing them in final layers, and increases low- or mid-wavelength power in the first-layer receptive fields.
no code implementations • 5 Sep 2021 • Francesco Fumarola, Bettina Hein, Kenneth D. Miller
For the brain to recognize local orientations within images, neurons must spontaneously break the translation and rotation symmetry of their response functions -- an archetypal example of unsupervised learning.
