DisRFM uses polar Riemannian flow matching on constant-curvature manifolds to align graph domains while preserving label-relevant topology via radial Wasserstein and angular confidence matching.
Domain-adversarial neural networks
5 Pith papers cite this work. Polarity classification is still indexing.
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abstract
We introduce a new representation learning algorithm suited to the context of domain adaptation, in which data at training and test time come from similar but different distributions. Our algorithm is directly inspired by theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on a data representation that cannot discriminate between the training (source) and test (target) domains. We propose a training objective that implements this idea in the context of a neural network, whose hidden layer is trained to be predictive of the classification task, but uninformative as to the domain of the input. Our experiments on a sentiment analysis classification benchmark, where the target domain data available at training time is unlabeled, show that our neural network for domain adaption algorithm has better performance than either a standard neural network or an SVM, even if trained on input features extracted with the state-of-the-art marginalized stacked denoising autoencoders of Chen et al. (2012).
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The paper categorizes five concrete AI safety problems arising from flawed objectives, costly evaluation, and learning dynamics.
CrossFlowDG bridges the modality gap in domain generalization by learning a continuous transformation that moves image embeddings to matching text embeddings using noise-free cross-modal flow matching.
Multi-purposing the domain discriminator to supply both domain-invariance and pseudo-label confidence scores in domain adaptation.
A domain-adversarial LSTM learns invariant features from labeled source RUL data to enable predictions in unlabeled target domains with distribution shifts due to varying conditions and fault modes.
citing papers explorer
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DisRFM: Polar Riemannian Flow Matching for Structure-Preserving Graph Domain Adaptation
DisRFM uses polar Riemannian flow matching on constant-curvature manifolds to align graph domains while preserving label-relevant topology via radial Wasserstein and angular confidence matching.
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Concrete Problems in AI Safety
The paper categorizes five concrete AI safety problems arising from flawed objectives, costly evaluation, and learning dynamics.
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CrossFlowDG: Bridging the Modality Gap with Cross-modal Flow Matching for Domain Generalization
CrossFlowDG bridges the modality gap in domain generalization by learning a continuous transformation that moves image embeddings to matching text embeddings using noise-free cross-modal flow matching.
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Multi-Purposing Domain Adaptation Discriminators for Pseudo Labeling Confidence
Multi-purposing the domain discriminator to supply both domain-invariance and pseudo-label confidence scores in domain adaptation.
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Remaining Useful Lifetime Prediction via Deep Domain Adaptation
A domain-adversarial LSTM learns invariant features from labeled source RUL data to enable predictions in unlabeled target domains with distribution shifts due to varying conditions and fault modes.