Neural Polarization

Neural polarization encompasses diverse research efforts leveraging the concept of separating or contrasting neural network activations to improve model performance and address specific challenges. Current research focuses on enhancing training stability and generalization in forward-only learning algorithms, improving the estimation of physical phenomena like subsurface scattering and electron density through polarization cues, and developing novel applications in image processing tasks such as reflection removal and backdoor defense. These advancements demonstrate the versatility of neural polarization across various domains, offering improved accuracy, efficiency, and robustness in diverse applications ranging from medical imaging to communications technology.