GGR is a gradient-space projection technique that rectifies auxiliary updates in open-set SSL to avoid first-order opposition with supervised learning while retaining orthogonal signals.
In: Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022)
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Geometric Gradient Rectification for Safe Open-Set Semi-Supervised Learning
GGR is a gradient-space projection technique that rectifies auxiliary updates in open-set SSL to avoid first-order opposition with supervised learning while retaining orthogonal signals.