1,163 research outputs found
Domain Adaptation via Minimax Entropy for Real/Bogus Classification of Astronomical Alerts
Time domain astronomy is advancing towards the analysis of multiple massive
datasets in real time, prompting the development of multi-stream machine
learning models. In this work, we study Domain Adaptation (DA) for real/bogus
classification of astronomical alerts using four different datasets: HiTS, DES,
ATLAS, and ZTF. We study the domain shift between these datasets, and improve a
naive deep learning classification model by using a fine tuning approach and
semi-supervised deep DA via Minimax Entropy (MME). We compare the balanced
accuracy of these models for different source-target scenarios. We find that
both the fine tuning and MME models improve significantly the base model with
as few as one labeled item per class coming from the target dataset, but that
the MME does not compromise its performance on the source dataset
Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy
Label scarcity in a graph is frequently encountered in real-world
applications due to the high cost of data labeling. To this end,
semi-supervised domain adaptation (SSDA) on graphs aims to leverage the
knowledge of a labeled source graph to aid in node classification on a target
graph with limited labels. SSDA tasks need to overcome the domain gap between
the source and target graphs. However, to date, this challenging research
problem has yet to be formally considered by the existing approaches designed
for cross-graph node classification. To tackle the SSDA problem on graphs, a
novel method called SemiGCL is proposed, which benefits from graph contrastive
learning and minimax entropy training. SemiGCL generates informative node
representations by contrasting the representations learned from a graph's local
and global views. Additionally, SemiGCL is adversarially optimized with the
entropy loss of unlabeled target nodes to reduce domain divergence.
Experimental results on benchmark datasets demonstrate that SemiGCL outperforms
the state-of-the-art baselines on the SSDA tasks
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
Generalizing deep neural networks to new target domains is critical to their
real-world utility. In practice, it may be feasible to get some target data
labeled, but to be cost-effective it is desirable to select a
maximally-informative subset via active learning (AL). We study the problem of
AL under a domain shift, called Active Domain Adaptation (Active DA). We
empirically demonstrate how existing AL approaches based solely on model
uncertainty or diversity sampling are suboptimal for Active DA. Our algorithm,
Active Domain Adaptation via Clustering Uncertainty-weighted Embeddings
(ADA-CLUE), i) identifies target instances for labeling that are both uncertain
under the model and diverse in feature space, and ii) leverages the available
source and target data for adaptation by optimizing a semi-supervised
adversarial entropy loss that is complementary to our active sampling
objective. On standard image classification-based domain adaptation benchmarks,
ADA-CLUE consistently outperforms competing active adaptation, active learning,
and domain adaptation methods across domain shifts of varying severity
Addressing Appearance Change in Outdoor Robotics with Adversarial Domain Adaptation
Appearance changes due to weather and seasonal conditions represent a strong
impediment to the robust implementation of machine learning systems in outdoor
robotics. While supervised learning optimises a model for the training domain,
it will deliver degraded performance in application domains that underlie
distributional shifts caused by these changes. Traditionally, this problem has
been addressed via the collection of labelled data in multiple domains or by
imposing priors on the type of shift between both domains. We frame the problem
in the context of unsupervised domain adaptation and develop a framework for
applying adversarial techniques to adapt popular, state-of-the-art network
architectures with the additional objective to align features across domains.
Moreover, as adversarial training is notoriously unstable, we first perform an
extensive ablation study, adapting many techniques known to stabilise
generative adversarial networks, and evaluate on a surrogate classification
task with the same appearance change. The distilled insights are applied to the
problem of free-space segmentation for motion planning in autonomous driving.Comment: In Proceedings of the 2017 IEEE/RSJ International Conference on
Intelligent Robots and Systems (IROS 2017
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