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Found 5 papers, 2 papers with code
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays
Real-world application of chest X-ray abnormality classification requires dealing with several challenges: (i) limited training data; (ii) training and evaluation sets that are derived from different domains; and (iii) classes that appear during training may have partial overlap with classes of interest during evaluation.
Task Attended Meta-Learning for Few-Shot Learning
The popular approaches for ML either learn a generalizable initial model or a generic parametric optimizer through episodic training.
On Characterizing GAN Convergence Through Proximal Duality Gap
Finally, we validate experimentally the usefulness of proximal duality gap for monitoring and influencing GAN training.
Stress Testing of Meta-learning Approaches for Few-shot Learning
Our results show a quick degradation in the performance of initialization strategies for ML (MAML, TAML, and MetaSGD), while surprisingly, approaches that use an optimization strategy (MetaLSTM) perform significantly better.
On Duality Gap as a Measure for Monitoring GAN Training
Further, we show that our estimate, with its ability to identify model convergence/divergence, is a potential performance measure that can be used to tune the hyperparameters of a GAN.
