Visualizing a Trained Autoencoder
From Ufldl
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parameters <math>\textstyle W^{(1)}_{ij}</math> (ignoring | parameters <math>\textstyle W^{(1)}_{ij}</math> (ignoring | ||
the bias term for now) using a 2D image. In particular, we think of | the bias term for now) using a 2D image. In particular, we think of | ||
- | <math>\textstyle a^{( | + | <math>\textstyle a^{(2)}_i</math> as some non-linear feature of the input <math>\textstyle x</math>. |
We ask: | We ask: | ||
What input image <math>\textstyle x</math> would cause | What input image <math>\textstyle x</math> would cause | ||
- | <math>\textstyle a^{( | + | <math>\textstyle a^{(2)}_i</math> to be maximally activated? |
(Less formally, what is the feature that hidden unit <math>\textstyle i</math> is looking for?) | (Less formally, what is the feature that hidden unit <math>\textstyle i</math> is looking for?) | ||
For this question to have a non-trivial answer, | For this question to have a non-trivial answer, |