MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly Annotation
Summary
MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly Annotation is a scholarly article[1].
Key Facts
MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly Annotation's instance of is recorded as scholarly article[2].
References
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APA4ort.xyz Knowledge Graph. (2026). MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly Annotation. Retrieved May 24, 2026, from https://4ort.xyz/entity/md-loss-efficient-training-of-3-d-seismic-fault-segmentation-network-under-sparse-labels-by-weakening-anomaly-annotation
MLA“MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly Annotation.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/md-loss-efficient-training-of-3-d-seismic-fault-segmentation-network-under-sparse-labels-by-weakening-anomaly-annotation.
BibTeX@misc{4ortxyz_md-loss-efficient-training-of-3-d-seismic-fault-segmentation-network-under-sparse-labels-by-weakening-anomaly-annotation_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly Annotation}}, year = {2026}, url = {https://4ort.xyz/entity/md-loss-efficient-training-of-3-d-seismic-fault-segmentation-network-under-sparse-labels-by-weakening-anomaly-annotation}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): MD Loss: Efficient Training of 3-D Seismic Fault Segmentation Network Under Sparse Labels by Weakening Anomaly Annotation — https://4ort.xyz/entity/md-loss-efficient-training-of-3-d-seismic-fault-segmentation-network-under-sparse-labels-by-weakening-anomaly-annotation (retrieved 2026-05-24)
