%20methods%2C%20renowned%20for%20their%20precision%20and%20speed%2C%20offer%20promising%20solutions%20for%20this%20complex%20task.%20However%2C%20traditional%20ML%20algorithms%20heavily%20depend%20on%20labeled%20data%20and%20task-specific%20feature%20extraction.%20To%20overcome%20these%20limitations%2C%20we%20introduce%20AstroMAE%2C%20an%20innovative%20approach%20that%20pretrains%20a%20vision%20transformer%20encoder%20using%20a%20masked%20autoencoder%20method%20on%20Sloan%20Digital%20Sky%20Survey%20(SDSS)%20images.%20This%20technique%20enables%20the%20encoder%20to%20capture%20the%20global%20patterns%20within%20the%20data%20without%20relying%20on%20labels.%20To%20the%20best%20of%20our%20knowledge%2C%20AstroMAE%20represents%20the%20first%20application%20of%20a%20masked%20autoencoder%20to%20astronomical%20data.%20By%20ignoring%20labels%20during%20the%20pretraining%20phase%2C%20the%20encoder%20gathers%20a%20general%20understanding%20of%20the%20data.%20The%20pretrained%20encoder%20is%20subsequently%20fine-tuned%20within%20a%20specialized%20architecture%20tailored%20for%20redshift%20prediction.%20We%20evaluate%20our%20model%20against%20various%20vision%20transformer%20architectures%20and%20CNN-based%20models%2C%20demonstrating%20the%20superior%20performance%20of%20AstroMAEs%20pretrained%20model%20and%20fine-tuning%20architecture.)
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