Project Description
Image domain adaptation through the conversion of images with randomized textures (or real images) to a canonical image representation. This project was a replication of a RCAN paper with different loss modeling.
Instead of using GAN loss, this implementation used Perceptual/Feature Loss for better training stability and results.
Technical Approach
- • Domain adaptation from randomized textures to canonical representations
- • Implementation of Residual Channel Attention Networks (RCAN)
- • Perceptual loss instead of adversarial training
- • Focus on sim-to-sim transfer for robotics applications