  We develop a new neural network based independent component analysis (ICA)
method by directly minimizing the dependence amongst all extracted components.
Using the matrix-based R{\'e}nyi's $\alpha$-order entropy functional, our
network can be directly optimized by stochastic gradient descent (SGD), without
any variational approximation or adversarial training. As a solid application,
we evaluate our ICA in the problem of hyperspectral unmixing (HU) and refute a
statement that "\emph{ICA does not play a role in unmixing hyperspectral
data}", which was initially suggested by \cite{nascimento2005does}. Code and
additional remarks of our DDICA is available at
https://github.com/hongmingli1995/DDICA.
