  Convolutional neural networks often generate multiple logits and use simple
techniques like addition or averaging for loss computation. But this allows
gradients to be distributed equally among all paths. The proposed approach
guides the gradients of backpropagation along weakest concept representations.
A weakness scores defines the class specific performance of individual pathways
which is then used to create a logit that would guide gradients along the
weakest pathways. The proposed approach has been shown to perform better than
traditional column merging techniques and can be used in several application
scenarios. Not only can the proposed model be used as an efficient technique
for training multiple instances of a model parallely, but also CNNs with
multiple output branches have been shown to perform better with the proposed
upgrade. Various experiments establish the flexibility of the learning
technique which is simple yet effective in various multi-objective scenarios
both empirically and statistically.
