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Found 5 papers, 2 papers with code
Data Mixture in Training Un-assures Out-of-Distribution Generalization
We study the problem of out-of-distribution (OOD) generalization capability of models by exploring the relationship between generalization error and training set size.
Diversity-enhancing Generative Network for Few-shot Hypothesis Adaptation
In this paper, we propose a diversity-enhancing generative network (DEG-Net) for the FHA problem, which can generate diverse unlabeled data with the help of a kernel independence measure: the Hilbert-Schmidt independence criterion (HSIC).
Domain Specified Optimization for Deployment Authorization
As a complementary of SDPA, we also propose Target-Combined Deployment Authorization (TPDA), where unauthorized domains are partially accessible, and simplify the DSO method to a perturbation operation on the pseudo predictions, referred to as Target-Dependent Domain-Specified Optimization (TDSO).
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation
To this end, we propose a target orientated hypothesis adaptation network (TOHAN) to solve the FHA problem, where we generate highly-compatible unlabeled data (i. e., an intermediate domain) to help train a target-domain classifier.
Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
In this paper, we demystify assumptions behind NCD and find that high-level semantic features should be shared among the seen and unseen classes.
