1 PyTorch implementation of the AdaIN architecture for real-time arbitrary style transfer. The code replicates the 2017 Huang et al. methodology for aligning feature statistics between content and style images.
Use Cases
- Develop real-time style transfer applications using the provided AdaIN layer logic
- Train an encoder-decoder network for arbitrary style transfer based on the 2017 Huang et al. architecture
- Perform arbitrary style transfer by swapping style images in the encoder-decoder pipeline
Strengths
- Implements the Adaptive Instance Normalization (AdaIN) layer for feature statistic alignment
- Replicates the architecture from the 2017 Huang et al. paper for arbitrary style transfer
- Developed using the PyTorch deep learning framework