Generative loss stuck

[tmralmeida]

Hi,

Regarding the Social GAN model and while playing with your code, I found something that I couldn't understand.

E.g while running:

python -m trajnetbaselines.sgan.trainer --k 1

It means that we are running a vanilla GAN where the generator outputs one sample (the most common GAN setting without the L2 loss); In doing so, the GAN loss is always 1.38 throughout the training. Thus, the vanilla GAN (with only the adversarial loss) is not capable of modeling the data.

My question is to what extent are we taking advantage of a GAN framework? It seems that we are only training an LSTM predictor (when running under the aforementioned conditions).