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Found 2 papers, 1 papers with code
Multi-Domain Learning with Modulation Adapters
Deep convolutional networks are ubiquitous in computer vision, due to their excellent performance across different tasks for various domains.
Meta-Learning with Shared Amortized Variational Inference
We propose a novel amortized variational inference scheme for an empirical Bayes meta-learning model, where model parameters are treated as latent variables.
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