Authoring

We address three key challenges in learning continuous kernel representations: computational efficiency, parameter efficiency, and spectral bias. Continuous kernels have shown significant potential, but their practical adoption is often limited by high computational and memory demands. Additionally, these methods are prone to spectral bias, which impedes their ability to capture high-frequency details. To overcome these limitations, we propose a novel approach that leverages sparse learning in the Fourier domain. Our method enables the efficient scaling of continuous kernels, drastically reduces computational and memory requirements, and mitigates spectral bias by exploiting the Gibbs phenomenon.

A hands on walk through the latent representation of stable diffiusion, targeted at improving understanding.

Comprehensive guide on training RetinaNet models for object detection with KerasCV.

Tutorial on implementing image classification models using KerasCV.

Use BaseImageAugmentationLayer to implement custom data augmentations.

Use KerasCV to augment images with CutMix, MixUp, RandAugment, and more.