The EΔ-MHC-Geo Transformer achieves input-adaptive unconditionally orthogonal residual connections via a Cayley-based rotation that works for all parameters, combined with a learned hybrid gate for reflections.
Deep residual learning for image recognition
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Data augmentation enables CNNs to adapt to varying architectures and data amounts without hyperparameter fine-tuning, unlike weight decay and dropout.
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The E$\Delta$-MHC-Geo Transformer: Adaptive Geodesic Operations with Guaranteed Orthogonality
The EΔ-MHC-Geo Transformer achieves input-adaptive unconditionally orthogonal residual connections via a Cayley-based rotation that works for all parameters, combined with a learned hybrid gate for reflections.
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Further advantages of data augmentation on convolutional neural networks
Data augmentation enables CNNs to adapt to varying architectures and data amounts without hyperparameter fine-tuning, unlike weight decay and dropout.