Based on this observation, we adopt ageneralized version ofthe JensenShannon divergence for multiple distributions to encourage consistency around data points. Using this loss function, we show state-of-the-art results on both synthetic(CIFAR),andreal-world(e.g.WebVision)noisewithvaryingnoiserates.

Compared meta-learning (2)results wecanonlymoutofM tasks.

Neural Information Processing Systems http://nips.cc/

We study whether and how can we model a joint distributionp(x,z)using two conditional modelsp(x|z) and q(z|x) that form a cycle.

Work done during an internship at Microsoft Research Asia.