As reported in Table A3, PS-MT consistently shows lower distances than Dual Teacher shows. The STD is similarly between 2 and over 50 times smaller. PS-MT's teachers (albeit they may have distinct characteristics) potentially becomes similar distances to the student at each epoch. Comparative analysis of performance based on different CutMix variations. We further report additional quantitative results encompassing three different splits: original high-quality set, blended set, and blended high-quality set .

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

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

If α > β, we are overemphasizing the contribution of the first term of Eq. 9 (which brings each layer'sλk1 andck close toeach other) overthesecond one(which induces small Lipschitz targets).

Fora adoptthesame ResultsonTable1 compares Cityscapes val set.

We propose a new regularization method to alleviate over-fitting in deep neural networks.

Deep neural networks (DNNs) are the de facto methodology in many artificial intelligence areas nowadays [25, 37]. How to effectively train a deep network has been a central topic for decades.