Fig. 4
From: Exploring neural architectures for simultaneously recognizing multiple visual attributes
The training (in blue) and validation (in orange) curves of \(Network_{two\;pathways}\) (left column) and \(Network_{one\;pathway}\) (right column) with 6000 total number of samples and three objects in each image. (a) The training and validation curves when training the orientation pathway in \(Network_{two\;pathways}\). (b) The training and validation curves when training the location pathway in \(Network_{two\;pathways}\). (c) The training and validation curves when training the luminance pathway in \(Network_{two\;pathways}\). (d) The training and validation curves when training the common dense layers in \(Network_{two\;pathways}\) for recognizing orientation and location. (e) The training and validation curves when training the common dense layers in \(Network_{two\;pathways}\) for recognizing luminance and location. (f) The training and validation curves of \(Network_{one\;pathway}\) for recognizing orientation and location. (g) The training and validation curves of \(Network_{one\;pathway}\) for recognizing luminance and location. According to (a,b,d), the total required number of training epochs for the two-pathway network for recognizing orientation and location is 110 (40 for training the orientation pathway, 10 for training the location pathway, 60 for training the common dense layers). According to (b,c,e), the total required number of training epochs for the two-pathway network for recognizing luminance and location is 170 (60 for training the orientation pathway, 10 for training the location pathway, 100 for training the common dense layers). According to (f,g), the one-pathway networks require 300 epochs to train in both cases.
