update README

README.md

@@ -126,7 +126,7 @@ bash ./datasets/download_pix2pix_dataset.sh dataset_name
 - `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com/). [[Citation](datasets/bibtex/cityscapes.tex)]
 - `maps`: 1096 training images scraped from Google Maps
 - `edges2shoes`: 50k training images from [UT Zappos50K dataset](http://vision.cs.utexas.edu/projects/finegrained/utzap50k/). Edges are computed by [HED](https://github.com/s9xie/hed) edge detector + post-processing. [[Citation](datasets/bibtex/shoes.tex)]
-- `edges2handbags`: 137K Amazon Handbag images from [iGAN project](https://github.com/junyanz/iGAN). Edges are computed by [HED](https://github.com/s9xie/hed) edge detector + post-processing. [[Citation](datasets/bibtex/iGAN.tex)]
+- `edges2handbags`: 137K Amazon Handbag images from [iGAN project](https://github.com/junyanz/iGAN). Edges are computed by [HED](https://github.com/s9xie/hed) edge detector + post-processing. [[Citation](datasets/bibtex/handbags.tex)]
 
 We provide a python script to generate pix2pix training data in the form of pairs of images {A,B}, where A and B are two different depictions of the same underlying scene. For example, these might be pairs {label map, photo} or {bw image, color image}. Then we can learn to translate A to B or B to A:
 

datasets/bibtex/iGAN.tex → datasets/bibtex/handbags.tex

@@ -4,3 +4,10 @@
   booktitle={Proceedings of European Conference on Computer Vision (ECCV)},
   year={2016}
 }
+
+@InProceedings{xie15hed,
+  author = {"Xie, Saining and Tu, Zhuowen"},
+  Title = {Holistically-Nested Edge Detection},
+  Booktitle = "Proceedings of IEEE International Conference on Computer Vision",
+  Year  = {2015},
+}

datasets/bibtex/shoes.tex

@@ -5,3 +5,10 @@
   month = {June},
   year = {2014}
 }
+
+@InProceedings{xie15hed,
+  author = {"Xie, Saining and Tu, Zhuowen"},
+  Title = {Holistically-Nested Edge Detection},
+  Booktitle = "Proceedings of IEEE International Conference on Computer Vision",
+  Year  = {2015},
+}