About this document
Learning Visual Domains with Residual Adapters by Nairouz Mrabah is a document available to read on EtoBox.
This paper presents a method for learning a single visual representation that effectively analyzes diverse image types using a tunable deep network architecture with residual adapter modules. The approach emphasizes parameter sharing across different visual domains while maintaining or enhancing accuracy, and introduces the Visual Decathlon Challenge as a benchmark for evaluating performance across ten varied visual classification tasks. The proposed architecture allows for dynamic adaptation to different d
- Author
- Nairouz Mrabah
- Language
- EN