This algorithm: sample a bunch of latents, train the model using the one with the lowest error.
IWAE: sample a bunch of latents, weight the loss of training the model using each one by softmax(-error). For images and text where the errors have large variance, those weights become one-hot, yielding this algorithm.
In what way is it similar?
This algorithm: sample a bunch of latents, train the model using the one with the lowest error.
IWAE: sample a bunch of latents, weight the loss of training the model using each one by softmax(-error). For images and text where the errors have large variance, those weights become one-hot, yielding this algorithm.
Yeah. This work is over claiming the novelty quite a bit.