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How do you guarantee that the two trained autoencoders speak the "same language", i.e. the code learned by the autoencoder of the original face gives a reasonab
by tralarpa 7y ago
How do you guarantee that the two trained autoencoders speak the "same language", i.e. the code learned by the autoencoder of the original face gives a reasonable output when uncompressed by the target autoencoder?
- claytonius 7y agoSomething like a CycleGAN and/ or weight sharing would do the trick
- orange3xchicken 7y agoThis is a good question. I'm not sure which paper the parent comment is referencing - and in general, DeepFakes is a very general term for a variety of techniques including GANs. The problem you describe is called the alignment problem and it's studied in several areas of applied ml: translation, multi-modal learning, etc. For example, the canonical way to train deep machine translation systems is to make use of large parallel corpora - large set of translation pairs. In the case where this data may not exist, for structurally similar languages, one approach is to learn unsupervised word embeddings independently (i.e. using word2vec) which only require monolingual data and to find a transformation that aligns the two spaces. In many cases, neural networks still struggle with this task. However, some work in the last few years has demonstrated that a few seed word-translations is all you need to apply simple algorithms that can learn a linear or simple nonlinear transformation that work pretty well! [1] Here is a method that proposes locally linear transformation maps for alignment: http://nakashole.com/papers/2018-emnlp-norm.pdf http://nakashole.com/papers/2018-emnlp-norm.pdf