Medical image classification is a challenging task due to the scarcity o...
End-to-End driving is a promising paradigm as it circumvents the drawbac...
In order to address real-world problems, deep learning models are jointl...
Deep learning models suffer from catastrophic forgetting of the classes ...
Deep learning models generally learn the biases present in the training ...
We propose a novel approach for class incremental online learning in a
l...
Deep learning models suffer from catastrophic forgetting when trained in...
Many real-world classification problems often have classes with very few...
Learning from a few examples is an important practical aspect of trainin...
In this paper, we propose an approach to improve few-shot classification...
This work presents a novel training technique for deep neural networks t...
In this paper, we solve for the problem of generalized zero-shot learnin...
Multiple categories of objects are present in most images. Treating this...
We present a filter pruning approach for deep model compression, using a...
Researchers have proposed various activation functions. These activation...
Convolutional layers are a major driving force behind the successes of d...
While convolutional neural networks (CNN) have achieved impressive
perfo...
Convolution Neural Networks (CNN) have been extremely successful in solv...
We present a novel deep learning architecture in which the convolution
o...
We present a filter correlation based model compression approach for dee...
We propose a framework for compressing state-of-the-art Single Shot Mult...
Convolutional neural networks (CNN) have achieved impressive performance...
An intelligent version of the sliding-puzzle game is developed using the...