Learning Invariant Representations with Local Transformations

Understand

Learning invariant representations is an important problem in machine learning and pattern recognition.

• In this paper, we present a novel framework of transformation-invariant feature learning by incorporating linear transformations into the feature learning algorithms.
• For example, we present the transformation-invariant restricted Boltzmann machine that compactly represents data by its weights and their transformations, which achieves invariance of the feature representation via probabilistic max pooling.
• In addition, we show that our transformation-invariant feature learning framework can also be extended to other unsupervised learning methods, such as autoencoders or sparse coding.