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Sparse auto-encoders (SAEs) are auto-encoders that impose constraints on the parameters so that they are sparse (i.e. zero or close to zero). This can be achieved in different ways. For example, you can train an auto-encoder with a loss function that includes a penalty term (to constraint the parameters to be close to zero or zero) or you e.g. set the ...


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According to various experimentation on autoencoders, it is very possible to have latent vector of size 1. Various layers can help the downsizing of the original input to a very small size of 1. But an issue may arise during decoding. If you're expecting that through one or two or maybe five layers in decoder you can achieve an accurate reconstruction, it is ...


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