34,532 research outputs found
Self-Adaptation for Unsupervised Domain Adaptation
Lack of labelled data in the target domain for training is a common problem in domain adaptation. To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches. Using the labelled data from the source domain, we first learn a projection that maximises the distance among the nearest neighbours with opposite labels in the source domain. Next, we project the source domain labelled data using the learnt projection and train a classifier for the target class prediction. We then use the trained classifier to predict pseudo labels for the target domain unlabelled data. Finally, we learn a projection for the target domain as we did for the source domain using the pseudo-labelled target domain data, where we maximise the distance between nearest neighbours having opposite pseudo labels. Experiments on a standard benchmark dataset for domain adaptation show that the proposed method consistently outperforms numerous baselines and returns competitive results comparable to that of SOTA including self-training, tri-training, and neural adaptations
From source to target and back: symmetric bi-directional adaptive GAN
The effectiveness of generative adversarial approaches in producing images
according to a specific style or visual domain has recently opened new
directions to solve the unsupervised domain adaptation problem. It has been
shown that source labeled images can be modified to mimic target samples making
it possible to train directly a classifier in the target domain, despite the
original lack of annotated data. Inverse mappings from the target to the source
domain have also been evaluated but only passing through adapted feature
spaces, thus without new image generation. In this paper we propose to better
exploit the potential of generative adversarial networks for adaptation by
introducing a novel symmetric mapping among domains. We jointly optimize
bi-directional image transformations combining them with target self-labeling.
Moreover we define a new class consistency loss that aligns the generators in
the two directions imposing to conserve the class identity of an image passing
through both domain mappings. A detailed qualitative and quantitative analysis
of the reconstructed images confirm the power of our approach. By integrating
the two domain specific classifiers obtained with our bi-directional network we
exceed previous state-of-the-art unsupervised adaptation results on four
different benchmark datasets
Domain Consistency Regularization for Unsupervised Multi-source Domain Adaptive Classification
Deep learning-based multi-source unsupervised domain adaptation (MUDA) has
been actively studied in recent years. Compared with single-source unsupervised
domain adaptation (SUDA), domain shift in MUDA exists not only between the
source and target domains but also among multiple source domains. Most existing
MUDA algorithms focus on extracting domain-invariant representations among all
domains whereas the task-specific decision boundaries among classes are largely
neglected. In this paper, we propose an end-to-end trainable network that
exploits domain Consistency Regularization for unsupervised Multi-source domain
Adaptive classification (CRMA). CRMA aligns not only the distributions of each
pair of source and target domains but also that of all domains. For each pair
of source and target domains, we employ an intra-domain consistency to
regularize a pair of domain-specific classifiers to achieve intra-domain
alignment. In addition, we design an inter-domain consistency that targets
joint inter-domain alignment among all domains. To address different
similarities between multiple source domains and the target domain, we design
an authorization strategy that assigns different authorities to domain-specific
classifiers adaptively for optimal pseudo label prediction and self-training.
Extensive experiments show that CRMA tackles unsupervised domain adaptation
effectively under a multi-source setup and achieves superior adaptation
consistently across multiple MUDA datasets
CARLANE: A Lane Detection Benchmark for Unsupervised Domain Adaptation from Simulation to multiple Real-World Domains
Unsupervised Domain Adaptation demonstrates great potential to mitigate
domain shifts by transferring models from labeled source domains to unlabeled
target domains. While Unsupervised Domain Adaptation has been applied to a wide
variety of complex vision tasks, only few works focus on lane detection for
autonomous driving. This can be attributed to the lack of publicly available
datasets. To facilitate research in these directions, we propose CARLANE, a
3-way sim-to-real domain adaptation benchmark for 2D lane detection. CARLANE
encompasses the single-target datasets MoLane and TuLane and the multi-target
dataset MuLane. These datasets are built from three different domains, which
cover diverse scenes and contain a total of 163K unique images, 118K of which
are annotated. In addition we evaluate and report systematic baselines,
including our own method, which builds upon Prototypical Cross-domain
Self-supervised Learning. We find that false positive and false negative rates
of the evaluated domain adaptation methods are high compared to those of fully
supervised baselines. This affirms the need for benchmarks such as CARLANE to
further strengthen research in Unsupervised Domain Adaptation for lane
detection. CARLANE, all evaluated models and the corresponding implementations
are publicly available at https://carlanebenchmark.github.io.Comment: 36th Conference on Neural Information Processing Systems (NeurIPS
2022) Track on Datasets and Benchmarks, 22 pages, 11 figure
TTA-COPE: Test-Time Adaptation for Category-Level Object Pose Estimation
Test-time adaptation methods have been gaining attention recently as a
practical solution for addressing source-to-target domain gaps by gradually
updating the model without requiring labels on the target data. In this paper,
we propose a method of test-time adaptation for category-level object pose
estimation called TTA-COPE. We design a pose ensemble approach with a
self-training loss using pose-aware confidence. Unlike previous unsupervised
domain adaptation methods for category-level object pose estimation, our
approach processes the test data in a sequential, online manner, and it does
not require access to the source domain at runtime. Extensive experimental
results demonstrate that the proposed pose ensemble and the self-training loss
improve category-level object pose performance during test time under both
semi-supervised and unsupervised settings. Project page:
https://taeyeop.com/ttacopeComment: Accepted to CVPR 2023, Project page: https://taeyeop.com/ttacop
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