21,264 research outputs found
Complementary Domain Adaptation and Generalization for Unsupervised Continual Domain Shift Learning
Continual domain shift poses a significant challenge in real-world
applications, particularly in situations where labeled data is not available
for new domains. The challenge of acquiring knowledge in this problem setting
is referred to as unsupervised continual domain shift learning. Existing
methods for domain adaptation and generalization have limitations in addressing
this issue, as they focus either on adapting to a specific domain or
generalizing to unseen domains, but not both. In this paper, we propose
Complementary Domain Adaptation and Generalization (CoDAG), a simple yet
effective learning framework that combines domain adaptation and generalization
in a complementary manner to achieve three major goals of unsupervised
continual domain shift learning: adapting to a current domain, generalizing to
unseen domains, and preventing forgetting of previously seen domains. Our
approach is model-agnostic, meaning that it is compatible with any existing
domain adaptation and generalization algorithms. We evaluate CoDAG on several
benchmark datasets and demonstrate that our model outperforms state-of-the-art
models in all datasets and evaluation metrics, highlighting its effectiveness
and robustness in handling unsupervised continual domain shift learning
Domain Adaptation in LiDAR Semantic Segmentation by Aligning Class Distributions
LiDAR semantic segmentation provides 3D semantic information about the
environment, an essential cue for intelligent systems during their decision
making processes. Deep neural networks are achieving state-of-the-art results
on large public benchmarks on this task. Unfortunately, finding models that
generalize well or adapt to additional domains, where data distribution is
different, remains a major challenge. This work addresses the problem of
unsupervised domain adaptation for LiDAR semantic segmentation models. Our
approach combines novel ideas on top of the current state-of-the-art approaches
and yields new state-of-the-art results. We propose simple but effective
strategies to reduce the domain shift by aligning the data distribution on the
input space. Besides, we propose a learning-based approach that aligns the
distribution of the semantic classes of the target domain to the source domain.
The presented ablation study shows how each part contributes to the final
performance. Our strategy is shown to outperform previous approaches for domain
adaptation with comparisons run on three different domains.Comment: 7 pages, 3 figure
Domain Generalization by Solving Jigsaw Puzzles
Human adaptability relies crucially on the ability to learn and merge
knowledge both from supervised and unsupervised learning: the parents point out
few important concepts, but then the children fill in the gaps on their own.
This is particularly effective, because supervised learning can never be
exhaustive and thus learning autonomously allows to discover invariances and
regularities that help to generalize. In this paper we propose to apply a
similar approach to the task of object recognition across domains: our model
learns the semantic labels in a supervised fashion, and broadens its
understanding of the data by learning from self-supervised signals how to solve
a jigsaw puzzle on the same images. This secondary task helps the network to
learn the concepts of spatial correlation while acting as a regularizer for the
classification task. Multiple experiments on the PACS, VLCS, Office-Home and
digits datasets confirm our intuition and show that this simple method
outperforms previous domain generalization and adaptation solutions. An
ablation study further illustrates the inner workings of our approach.Comment: Accepted at CVPR 2019 (oral
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