Custom implementation of PaletteNet in PyTorch
*PaletteNet: Image Recolorization with Given Color Palette (Cho et. al 2017) PaletteNet takes two inputs: a source image to be recolored and a target palette. PaletteNet is then designed to change the color concept of a source image so that the palette of the output image is close to the target palette
- Instance normalization in decoder path seemed to cause artifacts and was removed
- Transpose-convolutions replaced with upsampling convolutions to reduce checkerboard artifacts as shown in https://distill.pub/2016/deconv-checkerboard/
- No adversarial training yet (todo)
train.py: Train the network pre_process.py: Pre-process raw input data into original and augmented image-palette pairs and save in compressed npz format evaluate.py: Perform the inference given a source image and a target color-palette image
Input image:
Target palette:
Recolored image:
