Astroformer: More Data Might not be all you need for Classification

Understand

Recent advancements in areas such as natural language processing and computer vision rely on intricate and massive models that have been trained using vast amounts of unlabelled or partly labeled data and training or deploying these state-of-the-art methods to resource constraint environments has been a challenge.

• Galaxy morphologies are crucial to understanding the processes by which galaxies form and evolve.
• Efficient methods to classify galaxy morphologies are required to extract physical information from modern-day astronomy surveys.
• In this paper, we introduce Astroformer, a method to learn from less amount of data.