MFASSL adds mirror-paired views, a lightweight Mirror-Fusion Attention module, and reflection-consistency losses to improve SSL on bilateral data with ~2.7% extra parameters.
In: Interna- tional conference on machine learning
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Equivariant Poincaré ResNets combine hyperbolic geometry with C4 and D4 group symmetries via specialized reshaping, permutations, and batch norm to reduce optimization space and speed convergence while staying inside the Poincaré ball.
citing papers explorer
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Mirror-Fusion Attention for Reflection-Aware Self-Supervised Representation Learning
MFASSL adds mirror-paired views, a lightweight Mirror-Fusion Attention module, and reflection-consistency losses to improve SSL on bilateral data with ~2.7% extra parameters.
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Group-Equivariant Poincar\'e Convolutional Networks
Equivariant Poincaré ResNets combine hyperbolic geometry with C4 and D4 group symmetries via specialized reshaping, permutations, and batch norm to reduce optimization space and speed convergence while staying inside the Poincaré ball.