A Vision Transformer trained with interactive multi-class feedback and checked by citizen scientists finds 1,328 strong lens candidates in DES Year 6 data, including 147 new systems.
Recent Research Advances on Interactive Machine Learning
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abstract
Interactive Machine Learning (IML) is an iterative learning process that tightly couples a human with a machine learner, which is widely used by researchers and practitioners to effectively solve a wide variety of real-world application problems. Although recent years have witnessed the proliferation of IML in the field of visual analytics, most recent surveys either focus on a specific area of IML or aim to summarize a visualization field that is too generic for IML. In this paper, we systematically review the recent literature on IML and classify them into a task-oriented taxonomy built by us. We conclude the survey with a discussion of open challenges and research opportunities that we believe are inspiring for future work in IML.
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astro-ph.GA 1years
2025 1verdicts
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Discovering Strong Gravitational Lenses in the Dark Energy Survey with Interactive Machine Learning and Crowd-sourced Inspection with Space Warps
A Vision Transformer trained with interactive multi-class feedback and checked by citizen scientists finds 1,328 strong lens candidates in DES Year 6 data, including 147 new systems.