4 research outputs found
Learning to Learn with Variational Information Bottleneck for Domain Generalization
Domain generalization models learn to generalize to previously unseen
domains, but suffer from prediction uncertainty and domain shift. In this
paper, we address both problems. We introduce a probabilistic meta-learning
model for domain generalization, in which classifier parameters shared across
domains are modeled as distributions. This enables better handling of
prediction uncertainty on unseen domains. To deal with domain shift, we learn
domain-invariant representations by the proposed principle of meta variational
information bottleneck, we call MetaVIB. MetaVIB is derived from novel
variational bounds of mutual information, by leveraging the meta-learning
setting of domain generalization. Through episodic training, MetaVIB learns to
gradually narrow domain gaps to establish domain-invariant representations,
while simultaneously maximizing prediction accuracy. We conduct experiments on
three benchmarks for cross-domain visual recognition. Comprehensive ablation
studies validate the benefits of MetaVIB for domain generalization. The
comparison results demonstrate our method outperforms previous approaches
consistently.Comment: 15 pages, 4 figures, ECCV202
Discovering Latent Domains for Unsupervised Domain Adaptation Through Consistency
In recent years, great advances in Domain Adaptation (DA) have been possible through deep neural networks. While this is true even for multi-source scenarios, most of the methods are based on the assumption
that the domain to which each sample belongs is known a priori. However, in practice, we might have a source domain composed by a mixture of multiple sub-domains, without any prior about the sub-domain
to which each source sample belongs. In this case, while multi-source DA methods are not applicable, restoring to single-source ones may lead to sub-optimal results. In this work, we explore a recent direction in deep
domain adaptation: automatically discovering latent domains in visual datasets. Previous works address this problem by using a domain prediction branch, trained with an entropy loss. Here we present a novel
formulation for training the domain prediction branch which exploits (i) domain prediction output for various perturbations of the input features and (ii) the min-entropy consensus loss, which forces the predictions of the perturbation to be both consistent and with low entropy. We compare our approach to the previous state-of-the-art on publicly-available datasets, showing the effectiveness of our method both quantitatively and qualitatively