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Found 3 papers, 1 papers with code
SwitchLight: Co-design of Physics-driven Architecture and Pre-training Framework for Human Portrait Relighting
We introduce a co-designed approach for human portrait relighting that combines a physics-guided architecture with a pre-training framework.
Learning to Generalize to Unseen Tasks with Bilevel Optimization
To tackle this issue, we propose a simple yet effective meta-learning framework for metricbased approaches, which we refer to as learning to generalize (L2G), that explicitly constrains the learning on a sampled classification task to reduce the classification error on a randomly sampled unseen classification task with a bilevel optimization scheme.
Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks
While tasks could come with varying the number of instances and classes in realistic settings, the existing meta-learning approaches for few-shot classification assume that the number of instances per task and class is fixed.
