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Found 6 papers, 3 papers with code
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.
Multi-Class Deep SVDD: Anomaly Detection Approach in Astronomy with Distinct Inlier Categories
Our results demonstrate the efficacy of MCDSVDD in detecting anomalous sources while leveraging the presence of different inlier categories.
Con$^{2}$DA: Simplifying Semi-supervised Domain Adaptation by Learning Consistent and Contrastive Feature Representations
We use different loss functions to enforce consistency between the feature representations of associated data pairs of samples.
Alert Classification for the ALeRCE Broker System: The Real-time Stamp Classifier
We present a real-time stamp classifier of astronomical events for the ALeRCE (Automatic Learning for the Rapid Classification of Events) broker.
Matching Embeddings for Domain Adaptation
In this work we address the problem of transferring knowledge obtained from a vast annotated source domain to a low labeled target domain.
Deep-HiTS: Rotation Invariant Convolutional Neural Network for Transient Detection
We introduce Deep-HiTS, a rotation invariant convolutional neural network (CNN) model for classifying images of transients candidates into artifacts or real sources for the High cadence Transient Survey (HiTS).
