Enforcing equivariance reduces expressive power in 2-layer ReLU networks but enlarging the model compensates with proven size bounds and yields lower hypothesis space dimensionality for better generalization.
Se (3)-transformers: 3d roto-translation equivariant attention networks.Advances in neural information processing systems, 33:1970–1981,
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Drawback of Enforcing Equivariance and its Compensation via the Lens of Expressive Power
Enforcing equivariance reduces expressive power in 2-layer ReLU networks but enlarging the model compensates with proven size bounds and yields lower hypothesis space dimensionality for better generalization.