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Found 3 papers, 2 papers with code
Meta-Learning with Versatile Loss Geometries for Fast Adaptation Using Mirror Descent
Utilizing task-invariant prior knowledge extracted from related tasks, meta-learning is a principled framework that empowers learning a new task especially when data records are limited.
Scalable Bayesian Meta-Learning through Generalized Implicit Gradients
Meta-learning owns unique effectiveness and swiftness in tackling emerging tasks with limited data.
ADCC: An Effective and Intelligent Attention Dense Color Constancy System for Studying Images in Smart Cities
The augmented images help to tell apart the edge gradients, edge pixels and non-edge ones in log-histogram, which contribute significantly to the feature extraction and color-ambiguity elimination, thereby advancing the accuracy of illuminant estimation.
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