Best Self Supervised
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DINOv2 with ViT-g sets new accuracy records for self-supervised visual feature learning on multiple downstream tasks.
Noisy Student training with EfficientNet-L2 achieves state-of-the-art accuracy on ImageNet using self-training.
RoBERTa-Large improves upon BERT with more training data and longer training, achieving higher accuracy on GLUE and other benchmarks.
Swin-L introduces shifted windows for efficient attention, achieving top accuracy on ImageNet and other vision tasks.
Vision Transformer Large achieves competitive accuracy on ImageNet by applying transformer architecture directly to image patches.
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