Inverting facial recognition models

Re:Inverting facial recognition models by cathalh on Wednesday January 23, 2019 @03:05PM (#18982211)

Really interesting article. Whats fascinating is the way embeddings can be used for many NLP tasks such as translations and word embeddings like word2vec but can be also applied to image tasks like this.

For image tasks I read that adding more layers to the model helps find more complicated features of the images, i.e. first layer identifies edges, next layer finer outlines and so on. Would adding more layers in this model mean you need less epochs to make it more accurate or would there be any trade off like that?

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Re:Inverting facial recognition models by irhshafkat on Wednesday January 23, 2019 @03:13PM (#18982317)

If there is more information in the embedding than the baseline Generator is extracting, then adding in Convolutions between the Transpose Convolution blocks will certainly add more parameters the model could use to learn that.

Since the question is about training speed though, in general, larger models require more data which requires more time to train, so you'd actually increase both your data requirements, and time required to train.

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