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Facebook & UC Berkeley Substitute a Convolutional Stem to Dramatically Boost Vision Transformers’ Optimization Stability | Synced


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Recent studies have shown that vision transformer (ViT) models can attain better results than most state-of-the-art convolutional neural networks (CNNs) across various image recognition tasks, and can do so while using considerably fewer computational resources. This has led some researchers to propose ViTs could replace CNNs in this field.However, despite their promising performance, ViTs areContinue Reading

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