3 accepted papers
Convolutional transform learning is an unsupervised framework we introduced recently, for feature generation based on learnt convolutions. In this work, we propose a supervised formulation for convolutional transform so as to address the multi-label classification problem. Unlike the simple multicla…
We introduce deep transform learning - a new tool for deep learning. Deeper representation is learnt by stacking one transform after another. The model is akin to a feedforward neural network. The first layer learns the transform and features from the input training samples. Subsequent layers use th…
Dictionary learning follows a synthesis framework; the dictionary is learnt such that the data can be synthesized / re-generated from the coefficients. Transform learning on the other hand is based on analysis formulation; it learns a transform so as to generate the coefficients. The basic formulati…