update README

1 files changed, 8 insertions, 8 deletions

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+++ b/README.md
@@ -100,12 +100,12 @@ More example scripts can be found at `scripts` directory.
 
 
 ### CycleGAN Datasets
-Download the CycleGAN datasets using the following script:
+Download the CycleGAN datasets using the following script. Some of the datasets are collected by other researchers. Please cite their papers if you use the data.
 ```bash
 bash ./datasets/download_cyclegan_dataset.sh dataset_name
 ```
-- `facades`: 400 images from the [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade/).
-- `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com/).
+- `facades`: 400 images from the [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade/). [[Citation](datasets/bibtex/facades.tex)]
+- `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.
 - `horse2zebra`: 939 horse images and 1177 zebra images downloaded from [ImageNet](http://www.image-net.org/) using keywords `wild horse` and `zebra`
 - `apple2orange`: 996 apple images and 1020 orange images downloaded from [ImageNet](http://www.image-net.org/) using keywords `apple` and `navel orange`.
@@ -118,15 +118,15 @@ To train a model on your own datasets, you need to create a data folder with two
 You should **not** expect our method to work on just any random combination of input and output datasets (e.g. `cats<->keyboards`). From our experiments, we find it works better if two datasets share similar visual content. For example, `landscape painting<->landscape photographs` works much better than `portrait painting <-> landscape photographs`. `zebras<->horses` achieves compelling results while `cats<->dogs` completely fails.
 
 ### pix2pix datasets
-Download the pix2pix datasets using the following script:
+Download the pix2pix datasets using the following script. Some of the datasets are collected by other researchers. Please cite their papers if you use the data.
 ```bash
 bash ./datasets/download_pix2pix_dataset.sh dataset_name
 ```
-- `facades`: 400 images from [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade/).
-- `cityscapes`: 2975 images from the [Cityscapes training set](https://www.cityscapes-dataset.com/).
+- `facades`: 400 images from [CMP Facades dataset](http://cmp.felk.cvut.cz/~tylecr1/facade/). [[Citation](datasets/bibtex/facades.tex)]
+- `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.
-- `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.
+- `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)]
 
 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: