HyperGAN
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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

1
{
2
"class": "function:hypergan.train_hooks.experimental.rolling_memory_2_train_hook.RollingMemoryTrainHook",
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"types": ["mx-/g(mz-)"]
4
}
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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 1
integer
only
Overrides all other losses when this is set. Defaults to false
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
Last modified 1yr ago