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

Using the theoretical results, we provide a high-level understanding of howdifferentdesign choices ofMSDAworkdifferently.

Weshowthat thiscansuccessfully yield "one-shot" knowledge transfer (i.e, without requiring any retraining) between neural networks trained on heterogeneous non-i.i.d.

Whileprevious work tackles this issue by using explicit labeling on the spuriously correlated attributes or presuming a particular bias type, we instead utilize a cheaper, yet generic form of human knowledge, which can be widely applicable to various types ofbias.

However, if intrinsic parameters change, neural computations are altered drastically.

Tothis coordinate (1), and necessaryand solutionof (1). We usethemodeltrainedusing MixMatch [5] under 3 cases: (1) l = 100, u =1 , (2) = l = u = 100(reverse) and (3) = 100.