A conditional GAN implementing the pix2pix framework (Isola et al., 2017) for paired image-to-image translation. This model translates edge maps of handbags into realistic photographic renderings.
Model Description
Pix2Pix learns a mapping from input condition images to output target images using an adversarial training objective. The generator is supervised by both an adversarial loss (fooling the discriminator) and an L1 reconstruction loss (staying close to the ground truth).
Architecture
Generator — U-Net
Encoder: stacked Conv2d + BatchNorm2d + ReLU + MaxPool2d blocks, doubling channels at each stage
Decoder: Upsample (bilinear) + Conv2d blocks with skip connections from the corresponding encoder stage
Output: Tanh activation to produce pixel values in [-1, 1]
Discriminator — PatchGAN
Takes the concatenation of condition and real/fake image as input (6 channels)
Classifies whether overlapping image patches are real or generated
