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Found 9 papers, 1 papers with code
Improved Generalization Risk Bounds for Meta-Learning with PAC-Bayes-kl Analysis
In particular, we identify that when the number of training task is large, utilizing a prior generated from an informative hyperposterior can achieve the same order of PAC-Bayes-kl bound as that obtained through setting a localized distribution-dependent prior for a novel task.
Task Relatedness-Based Generalization Bounds for Meta Learning
Supposing the $n$ training tasks and the new task are sampled from the same environment, traditional meta learning theory derives an error bound on the expected loss over the new task in terms of the empirical training loss, uniformly over the set of all hypothesis spaces.
Margin-Based Transfer Bounds for Meta Learning with Deep Feature Embedding
By transferring knowledge learned from seen/previous tasks, meta learning aims to generalize well to unseen/future tasks.
Domain-Adaptive Few-Shot Learning
Existing few-shot learning (FSL) methods make the implicit assumption that the few target class samples are from the same domain as the source class samples.
Few-Shot Learning as Domain Adaptation: Algorithm and Analysis
Specifically, armed with a set transformer based attention module, we construct each episode with two sub-episodes without class overlap on the seen classes to simulate the domain shift between the seen and unseen classes.
Zero-Shot Learning with Sparse Attribute Propagation
The standard approach to ZSL requires a set of training images annotated with seen class labels and a semantic descriptor for seen/unseen classes (attribute vector is the most widely used).
Zero and Few Shot Learning with Semantic Feature Synthesis and Competitive Learning
Specifically, we assume that each synthesised data point can belong to any unseen class; and the most likely two class candidates are exploited to learn a robust projection function in a competitive fashion.
Transferrable Feature and Projection Learning with Class Hierarchy for Zero-Shot Learning
Inspired by the fact that an unseen class is not exactly `unseen' if it belongs to the same superclass as a seen class, we propose a novel inductive ZSL model that leverages superclasses as the bridge between seen and unseen classes to narrow the domain gap.
Domain-Invariant Projection Learning for Zero-Shot Recognition
This is made possible by learning a projection between a feature space and a semantic space (e. g. attribute space).
