discriminators

the ai model used discriminators to distinguish between real and fake images.

we trained the neural network with multiple discriminators for improved performance.

the discriminator's role is to evaluate the generator's output in gans.

adversarial training involves a generator and a discriminator competing against each other.

the discriminator provided valuable feedback to the generator during training.

we compared the performance of different discriminator architectures.

the discriminator learned to identify subtle differences in the data.

a well-designed discriminator is crucial for successful gan training.

the discriminator's loss function guided the generator's learning process.

we used a convolutional discriminator for image generation tasks.

the discriminator's accuracy improved as the training progressed.