Design and Interpretation of a Convolutional Neural Network Architecture for Imaging Mass Spectrometry Data

Convolutional Neural Networks (CNNs) have emerged primarily from research focusing on image classification tasks and as a result, most of the well-motivated design choices found in literature are relevant to computer vision applications. CNNs' application on Imaging Mass Spectrometry (IMS) data is quite recent and involves new challenges, such...

Go Deep or Go Home?

Does a convolutional neural network (CNN) always have to be deep to learn a task? This is an important question as deeper networks are generally harder to train. We trained shallow and deep CNNs and evaluated their performance on simple regression tasks, such as computing the mean pixel value of an image. For these simple tasks we show that...

Weight Swapping: A new method for Supervised Domain Adaptation in Computer Vision using Discrete Optimization

Training Convolutional Neural Network (CNN) models is difficult when there is a lack of labeled training data and no unlabeled data is available. A popular method for this is domain adaptation where the weights of a pre-trained CNN model are transferred to the problem setup. The model is pre-trained on the same task but in a different domain...