However,evenwhentheprior is useful for generalization, distilling it to RL agent often interferes with RL training and degenerates sample efficiency.

However,evenwhentheprior is useful for generalization, distilling it to RL agent often interferes with RL training and degenerates sample efficiency.

The codes related todataset, model, loss, training pipeline and experiment areenclosed. Cross-Domain MAFLAFLWMAFLWR 300W Supervised learning TCDCN[13] XX 7.95 7.65 - 5.54 MTCNN[12] XX 5.39 6.90 - WingLoss[3] XX - - - 4.04 Generative modeling based DeformingAE[9] OX 5.45 - - ImGen.[4] After the initialization period, the intra pseudo-paired dataxd1)d1, xd2)d2 and inter pseudo-paired dataxd1)d2,xd2)d1 aregenerated with latent space exploration described atSection 3.2. Atlastsemanticmatchingloss LM are utilized to get intra semantic matching lossLM1 and inter semantic matching lossLM2. We provide more examples of pseudo-paired data on various combinations of original and pair domainsinFig.3.

Thenwesynthesizepseudo pair images through the latent space exploration of StyleGAN2 to find implicit associations between different animal faces. Finally, we introduce the semantic matching loss to overcome the problem of extreme shape differences between species.

We present a novel introspective variational autoencoder (IntroVAE) model for synthesizing high-resolution photographic images. IntroVAE is capable of selfevaluating the quality of its generated samples and improving itself accordingly.

RL agents learn policies from experience.

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