Frame Averaging

Frame averaging is a technique used in machine learning to efficiently incorporate symmetries present in data into neural network models, ensuring that the model's output transforms consistently with the input under these symmetries (equivariance). Current research focuses on developing mathematically rigorous and computationally efficient frame averaging methods, including exploring minimal frames and weighted averaging to address issues like discontinuity and improve accuracy. This approach offers a flexible and powerful alternative to designing explicitly equivariant architectures, leading to improved performance and generalization in diverse applications such as materials modeling, particle physics, and shape analysis.