66,707 research outputs found
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
Cross-Domain Grouping and Alignment for Domain Adaptive Semantic Segmentation
Existing techniques to adapt semantic segmentation networks across the source
and target domains within deep convolutional neural networks (CNNs) deal with
all the samples from the two domains in a global or category-aware manner. They
do not consider an inter-class variation within the target domain itself or
estimated category, providing the limitation to encode the domains having a
multi-modal data distribution. To overcome this limitation, we introduce a
learnable clustering module, and a novel domain adaptation framework called
cross-domain grouping and alignment. To cluster the samples across domains with
an aim to maximize the domain alignment without forgetting precise segmentation
ability on the source domain, we present two loss functions, in particular, for
encouraging semantic consistency and orthogonality among the clusters. We also
present a loss so as to solve a class imbalance problem, which is the other
limitation of the previous methods. Our experiments show that our method
consistently boosts the adaptation performance in semantic segmentation,
outperforming the state-of-the-arts on various domain adaptation settings.Comment: AAAI 202
Self-adjustable domain adaptation in personalized ECG monitoring integrated with IR-UWB radar
To enhance electrocardiogram (ECG) monitoring systems in personalized detections, deep neural networks (DNNs) are applied to overcome individual differences by periodical retraining. As introduced previously [4], DNNs relieve individual differences by fusing ECG with impulse radio ultra-wide band (IR-UWB) radar. However, such DNN-based ECG monitoring system tends to overfit into personal small datasets and is difficult to generalize to newly collected unlabeled data. This paper proposes a self-adjustable domain adaptation (SADA) strategy to prevent from overfitting and exploit unlabeled data. Firstly, this paper enlarges the database of ECG and radar data with actual records acquired from 28 testers and expanded by the data augmentation. Secondly, to utilize unlabeled data, SADA combines self organizing maps with the transfer learning in predicting labels. Thirdly, SADA integrates the one-class classification with domain adaptation algorithms to reduce overfitting. Based on our enlarged database and standard databases, a large dataset of 73200 records and a small one of 1849 records are built up to verify our proposal. Results show SADA\u27s effectiveness in predicting labels and increments in the sensitivity of DNNs by 14.4% compared with existing domain adaptation algorithms
- …