Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification

• URL: http://arxiv.org/abs/2607.05393v1
• Date: Mon, 06 Jul 2026 17:59:58 GMT
• Title: Interpretable Human-Label-Free Deep Learning for Real-Bogus Classification with Uncertainty Quantification
• Abstract summary: Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines.<n>We develop a Real-Bogus classification framework that can be trained without human-labeled data using injected transients and bogus-dominated survey data.
• Score: 2.687159171905859
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Time-domain surveys generate many transient candidates, making Real-Bogus classification a critical step in automated discovery pipelines. Reliable labels are costly, while community labels can be noisy and survey-dependent. We aim to develop a Real-Bogus classification framework that can be trained without human-labeled data using injected transients and bogus-dominated survey data, remains robust under strong class contamination, and provides calibrated uncertainty quantification. We combine simulated transient injections with a contaminated survey class and train a dual-network model using asymmetric co-teaching for classes with different label-noise levels. We evaluate performance on a benchmark subset and analyze the learned representation with latent-space visualization tools. For uncertainty quantification (UQ), we compare MC dropout and deep ensembles and propose a low-cost hybrid strategy that exploits the dual-network setting to improve calibration. We extend the evaluation to the light-curve domain to assess recovery of light-curve classes. The method achieves strong Real-Bogus performance on the labeled subset and remains stable under severe class contamination. It recovers transient light-curve classes with high fidelity, while single-source identification is limited by ambiguity in light-curve-derived labels. Our hybrid UQ approach achieves competitive calibration relative to more expensive ensemble baselines. Latent-space analyses indicate that uncertainty aligns with the decision boundary and reveal subclasses within the bogus population. Our results show that injection-driven, weakly supervised training can enable scalable and consistent Real-Bogus classification without human-labeled training data while providing calibrated uncertainties. The method is suited for transfer to forthcoming surveys by re-running the injection-based training pipeline.

Related papers

• Architecture-agnostic Lipschitz-constant Bayesian header and its application to resolve semantically proximal classification errors with vision transformers [0.0]
  This work presents an architecture-agnostic Lipschitz-constant Bayesian header that can be integrated into feature extractors such as vision transformers.<n>We also propose a novel metric to capture uncertainty and confidence across misclassification rates, as well as an adaptive arithmetic-mean fusion scheme.<n>Although computational costs increase due to Monte Carlo sampling, the method offers plug-and-play compatibility with pre-trained backbones.
  arXiv  Detail & Related papers  (2026-05-07T09:18:06Z)
• Online Bayesian Imbalanced Learning with Bregman-Calibrated Deep Networks [0.7106986689736825]
  We present textitOnline Bayesian Imbalanced Learning (OBIL), a principled framework that decouples likelihood-ratio estimation from class-prior assumptions.<n>Our approach builds on the established connection between Bregman divergences and proper scoring rules to show that deep networks trained with such losses produce posterior probability estimates.<n>We prove that these likelihood-ratio estimates remain valid under arbitrary changes in class priors and cost structures, requiring only a threshold adjustment for optimal Bayes decisions.
  arXiv  Detail & Related papers  (2026-02-08T21:23:00Z)
• Combating Noisy Labels through Fostering Self- and Neighbor-Consistency [120.4394402099635]
  Label noise is pervasive in various real-world scenarios, posing challenges in supervised deep learning.<n>We propose a noise-robust method named Jo-SNC (textbfJoint sample selection and model regularization based on textbfSelf- and textbfNeighbor-textbfConsistency)<n>We design a self-adaptive, data-driven thresholding scheme to adjust per-class selection thresholds.
  arXiv  Detail & Related papers  (2026-01-19T07:55:29Z)
• Pseudo-D: Informing Multi-View Uncertainty Estimation with Calibrated Neural Training Dynamics [3.8836478189829164]
  One-hot labels erase inter-rater variability and force models to make overconfident predictions.<n>We introduce a novel framework that brings uncertainty back into the label space.
  arXiv  Detail & Related papers  (2025-09-15T11:30:12Z)
• Noisy Correspondence Learning with Self-Reinforcing Errors Mitigation [63.180725016463974]
  Cross-modal retrieval relies on well-matched large-scale datasets that are laborious in practice. We introduce a novel noisy correspondence learning framework, namely textbfSelf-textbfReinforcing textbfErrors textbfMitigation (SREM)
  arXiv  Detail & Related papers  (2023-12-27T09:03:43Z)
• Semi-Supervised Class-Agnostic Motion Prediction with Pseudo Label Regeneration and BEVMix [59.55173022987071]
  We study the potential of semi-supervised learning for class-agnostic motion prediction. Our framework adopts a consistency-based self-training paradigm, enabling the model to learn from unlabeled data. Our method exhibits comparable performance to weakly and some fully supervised methods.
  arXiv  Detail & Related papers  (2023-12-13T09:32:50Z)
• SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-supervised Learning [101.86916775218403]
  This paper revisits the popular pseudo-labeling methods via a unified sample weighting formulation. We propose SoftMatch to overcome the trade-off by maintaining both high quantity and high quality of pseudo-labels during training. In experiments, SoftMatch shows substantial improvements across a wide variety of benchmarks, including image, text, and imbalanced classification.
  arXiv  Detail & Related papers  (2023-01-26T03:53:25Z)
• Semi-supervised Long-tailed Recognition using Alternate Sampling [95.93760490301395]
  Main challenges in long-tailed recognition come from the imbalanced data distribution and sample scarcity in its tail classes. We propose a new recognition setting, namely semi-supervised long-tailed recognition. We demonstrate significant accuracy improvements over other competitive methods on two datasets.
  arXiv  Detail & Related papers  (2021-05-01T00:43:38Z)
• Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification [137.9939571408506]
  We estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
  arXiv  Detail & Related papers  (2020-12-16T04:09:04Z)
• Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification [29.43017692274488]
  Deep predictive models rely on human supervision in the form of labeled training data. We propose Ask-n-Learn, an active learning approach based on gradient embeddings obtained using the pesudo-labels estimated in each of the algorithm.
  arXiv  Detail & Related papers  (2020-09-30T05:19:56Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.