Feb 8, 2022 · We propose a novel weak supervision algorithm that processes noisy labels, i.e., weak signals, while also considering features of the training ...
We propose a novel weak supervision algorithm that processes noisy labels, i.e., weak signals, while also considering features of the training data to produce ...
Sep 6, 2024 · We call this paradigm data consistent weak supervision. A key facet of our framework is that we are able to estimate labels for data examples ...
A novel weak supervision algorithm is proposed that processes noisy labels while also considering features of the training data to produce accurate labels ...
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We propose a novel weak supervision algorithm that processes noisy labels, i.e., weak signals, while also considering features of the training data to produce ...
Feb 8, 2022 · We set. Page 4. Data Consistency for Weakly Supervised Learning the slack penalty C = 10. We run CLL with the same bounds as ours, π = 0, but we ...
Aug 17, 2024 · In this paper, we propose a weakly supervised paradigm of cross-modal detection and consistency learning, leveraging dual consistency to provide discriminative ...
Jan 18, 2024 · A typical example is the crowdsourcing of data labels, which reduces the cost of data preparation compared with labeling by one or more experts.
While consistency methods have been successfully deployed in semantic segmentation, the novel aspect of this work is the notion of consistency under weak ...
This paper introduces a carefully-designed approach for learning class agnostic and specific consistency, based on the teacher–student framework.