PrivacyNet results: obfuscated face images

Outputs of the PrivacyNet model as well as the baseline-GAN model on CelebA-test dataset

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## Note:

 * when we obfuscate gender, the gender of a face image is flipped to the opposite gender, the same for ra

ce. But for age, since we have 3 labels (A1: young, A2: middle-aged, A3: old), we specify the target for t

he transformation, so all images are transformed to the target label irrespective of their original label.

### Directories

 * out-0: no change

 * out-1: obfuscate gender (male -> female and female -> male)

 * out-2: obfuscate age (target age is A1, i.e., young)

 * out-3: obfuscate age (target age is A2, i.e., middle-aged)

 * out-4: obfuscate age (target age is A3, i.e., old)

 * out-5: obfuscate race

 * out-6: obfuscate gender and age (target age is A1, i.e., young)

 * out-7: obfuscate gender and age (target age is A2, i.e., middle-aged)

 * out-8: obfuscate gender and age (target age is A3, i.e., old)

 * out-9: obfuscate gender and race

 * out-10: obfuscate race and age (target age is A1, i.e., young)

 * out-11: obfuscate race and age (target age is A2, i.e., middle-aged)

 * out-12: obfuscate race and age (target age is A3, i.e., old)

 * out-13: obfuscate gender, race and age (target age is A1, i.e., young)

 * out-14: obfuscate gender, race and age (target age is A2, i.e., middle-aged)

 * out-15: obfuscate gender, race and age (target age is A3, i.e., old)