Learning Discriminative Features using Encoder-Decoder type Deep Neural Nets

TL;DR

This paper introduces a novel encoder-decoder deep neural network architecture for learning discriminative features, especially effective in small sample size classification tasks.

Contribution

It proposes a new deep learning method that enhances discriminative feature learning using encoder-decoder architectures similar to autoencoders.

Findings

Improved classification performance with small training datasets.

Effective feature learning through encoder-decoder architectures.

Demonstrated superiority over traditional methods in specific tasks.

Abstract

As machine learning is applied to an increasing variety of complex problems, which are defined by high dimensional and complex data sets, the necessity for task oriented feature learning grows in importance. With the advancement of Deep Learning algorithms, various successful feature learning techniques have evolved. In this paper, we present a novel way of learning discriminative features by training Deep Neural Nets which have Encoder or Decoder type architecture similar to an Autoencoder. We demonstrate that our approach can learn discriminative features which can perform better at pattern classification tasks when the number of training samples is relatively small in size.