4 papers
Unravelling Small Sample Size Problems in the Deep Learning World
Rohit Keshari, Soumyadeep Ghosh, Saheb Chhabra +2
The growth and success of deep learning approaches can be attributed to two major factors: availability of hardware resources and availability of large number of training samples.…
Generalized Zero-Shot Learning Via Over-Complete Distribution
Rohit Keshari, Richa Singh, Mayank Vatsa
A well trained and generalized deep neural network (DNN) should be robust to both seen and unseen classes. However, the performance of most of the existing supervised DNN algorithm…
Guided Dropout
Rohit Keshari, Richa Singh, Mayank Vatsa
Dropout is often used in deep neural networks to prevent over-fitting. Conventionally, dropout training invokes \textit{random drop} of nodes from the hidden layers of a Neural Net…
Learning Structure and Strength of CNN Filters for Small Sample Size Training
Rohit Keshari, Mayank Vatsa, Richa Singh +1
Convolutional Neural Networks have provided state-of-the-art results in several computer vision problems. However, due to a large number of parameters in CNNs, they require a large…