text: 326,48,1396912380039286791,2693638267,Arjun (Raj) Manrai,"Deep learning approaches for semantic image segmentation are often data hungry or difficult to train. Thrilled to share our new approach called PixMatch, our #CVPR21 paper now on arXiv. Led by the singular @lukemelas: Paper: <LINK> Code: <LINK> <LINK> PixMatch is a new approach to unsupervised domain adaptation for semantic segmentation. It exploits the idea that in order to perform well on the target domain, a model’s output should be consistent with respect to small perturbations of inputs in the target domain. Read more here: <LINK> and keep an eye on @lukemelas 🚀, a wonderful member of my group and a truly exceptional scientist. Check out some of @lukemelas' other work here: <LINK> <LINK>",https://arxiv.org/abs/2105.08128,"Unsupervised domain adaptation is a promising technique for semantic segmentation and other computer vision tasks for which large-scale data annotation is costly and time-consuming. In semantic segmentation, it is attractive to train models on annotated images from a simulated (source) domain and deploy them on real (target) domains. In this work, we present a novel framework for unsupervised domain adaptation based on the notion of target-domain consistency training. Intuitively, our work is based on the idea that in order to perform well on the target domain, a model's output should be consistent with respect to small perturbations of inputs in the target domain. Specifically, we introduce a new loss term to enforce pixelwise consistency between the model's predictions on a target image and a perturbed version of the same image. In comparison to popular adversarial adaptation methods, our approach is simpler, easier to implement, and more memory-efficient during training. Experiments and extensive ablation studies demonstrate that our simple approach achieves remarkably strong results on two challenging synthetic-to-real benchmarks, GTA5-to-Cityscapes and SYNTHIA-to-Cityscapes. Code is available at: this https URL ","PixMatch: Unsupervised Domain Adaptation via Pixelwise Consistency