YinYang-GAN: Phase Lock + Constructionism + GAN + Cross-Modality + Iterative Inference
structure illustration:
[x_i in P_i, i=0,1,...,M;$$ $x_i$:sample, $P$:pattern space, $M$:number of spaces.
$$hat{x}_i = G_i(x_i) = EC_i(DC_i(x_i));$$ $DC$: decoder, $EC$: encoder, $G$: generator.
$$D_i(x_i, hat{x}_i) in {0,1};$$ $hat{x}_i$:generated sample, $D_i$:discriminator.
$$z_i = DC_i(x_i);$$ $z_i$: latent code decoded from $x_i$ with $DC_i$.
$$z'_i = sum_{j
eq{i}}{T_{ji}(T_{ij}(z_i))};$$ $T_{ji}$:Translator from $j$ to $i$.
$$hat{x'}_i = EC_i(z'_i);$$ $z'_i$:combined latent code, $hat{x'}_i$:final output.
$$frac{partial{D_i(x_i, hat{x'}_i)}}{partial{z_i}}.$$ Differential to optimize on $z_i$.
Approach#1: training a AutoEncoder instead of training a unstable GAN.
Average Instance: for each given associated observation $z_j(j
eq{i})$, there is an dynamic average instance $ar{z}_i=T_{j
ightarrow{i}}(z_j)$.
For instance, let label of 'Lady' be an associated observation, the visual compensation will be a slim body with long hair, it stands for the average instance of 'Lady' in visual space.]