#multimodal learning

Explore the latest developments in visual representation from the EVA projects at BAAI, featuring models like EVA-01 for large-scale masked learning and EVA-CLIP-18B with 18 billion parameters CLIP scaling. Learn about advanced training techniques and their impact on multimodal learning, offering insights valuable for researchers and engineers interested in foundational and self-supervised learning.

PyKale supports machine learning in different data types like graphs, images, and videos. It integrates multimodal and transfer learning with a focus on green principles through standardization and minimalism. PyKale's design facilitates reduced redundancy and efficient resource use, promoting accurate and interpretable outcomes. The library offers a fluid pipeline-based API ensuring smooth project integration, especially useful in fields like bioinformatics and medical imaging.