add tnnls

README.md

@@ -67,6 +67,7 @@ The proposed generator learns both foreground and background attentions. It uses
 AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks.<br>
 [Hao Tang](http:https://disi.unitn.it/~hao.tang/)<sup>1</sup>, [Hong Liu](https://scholar.google.com/citations?user=4CQKG8oAAAAJ&hl=en)<sup>2</sup>, [Dan Xu](http:https://www.robots.ox.ac.uk/~danxu/)<sup>3</sup>, [Philip H.S. Torr](https://scholar.google.com/citations?user=kPxa2w0AAAAJ&hl=en)<sup>3</sup> and [Nicu Sebe](http:https://disi.unitn.it/~sebe/)<sup>1</sup>. <br> 
 <sup>1</sup>University of Trento, Italy, <sup>2</sup>Peking University, China, <sup>3</sup>University of Oxford, UK.<br>
+In TNNLS 2021 & IJCNN 2019 Oral. <br>
 The repository offers the official implementation of our paper in PyTorch.
 
 #### Are you looking for AttentionGAN-v1 for Unpaired Image-to-Image Translation?