title: Differentiable Canonical Correlation Analysis
abstract: Canonical Correlation Analysis (CCA) computes maximally-correlated 
  linear projections of two modalities. We propose Differentiable CCA, a 
  formulation of CCA that can be cast as a layer within a multi-view 
  neural network. Unlike Deep CCA, an earlier extension of CCA to 
  nonlinear projections, our formulation enables gradient flow through the 
  computation of the CCA projection matrices, and free choice of the final 
  optimization target. We show the effectiveness of this approach in 
  cross-modality retrieval experiments on two public image-to-text 
  datasets, surpassing both Deep CCA and a multi-view network with 
  freely-learned projections. We assume that Differentiable CCA could be a 
  useful building block for many multi-modality tasks.
authors: Matthias Dorfer Jan Schlüter Gerhard Widmer
hIndex: Gerhard Widmer 50