Rolling Memory
(no paper)
Rolling memory is a type of experience replay. Each training step, a memory is replaced with the top scoring batch item.
Each types
pairing becomes a discriminator that is added to the loss.
examples
mx- is a memory of x that gets updated each training step. g(mz-) is a memory of z that gets run through a generator and updated each trainng step.
A discriminator d(mx-, g(mz-))
is created and added to the gan loss.
options
attribute | description | type |
types | What memories and how they are paired. See memory types below | array of strings |
top_k | How many memory items to replace per frame. Defaults to | integer |
only | Overrides all other losses when this is set. Defaults to | boolean |
memory types
memory | description |
mx- | x reverse sorted by d_real |
mx+ | x sorted by d_real |
mg- | memory of g reverse sorted by d_fake |
mg+ | memory of g sorted by d_fake |
g(mz-) | generator of memory of z reverse sorted by d_fake |
g(mz+) | generator of memory of z sorted by d_fake |
x | gan.inputs.x |
g | gan.generator.sample |
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