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About the y_likelihoods #306
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Please see the following two sections:
Briefly, each element of y is encoded using a probability distribution p. The y_likelihoods represent the value p(y).
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Thank you for your reply! I really appreciate it!!I still have some issues to ask: |
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Thank you so much! I have another question:how can I calculate the y_likelihoods if I don't use quantization(add uniform noise)(Because I want to apply it to my research domain) |
Sorry to bother you again.The element of y_likelihoods represents the value p(yi).Is p(yi) the probability that the symbol yi appears? If so,how is it ensured that the sum of the probabilities of all symbols appearing is equal to 1? |
For the But how is
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Sorry to bother you, I am recently been in the area of compression, take VARIATIONAL IMAGE COMPRESSION WITH A SCALE HYPERPRIOR as example, what is the meaning of each element in y_likelihoods matrix("likelihoods": {"y": y_likelihoods})?
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