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Hey Srikanth, this GAN outputs discrete tokens, unlike regular GANs, so a regular adversarial loss will not work. To train the model, Policy Gradient is used, where feedback is given by the discriminator:
Also, if you have any other questions, I will try my best to explain:)
I keep procrastinating and end up not writing a proper readme for this repo, but I aim to do that soon. Overall, the model takes ages to train and improvements in performance are not huge; however, it was an excellent learning opportunity for me.
Hi @Anjaney1999 ,
I was looking at your code and trying to find adversarial loss in the generator training scheme:
image-captioning-seqgan/train_pg.py
Line 285 in 10e60ad
Can you let me know if it is used in your code? If not, it is need for GAN right? Please let me know.
Thank you,
Srikanth
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