all AI news
Efficient Training of 3D Seismic Image Fault Segmentation Network under Sparse Labels by Weakening Anomaly Annotation. (arXiv:2110.05319v5 [cs.CV] UPDATED)
cs.CV updates on arXiv.org arxiv.org
Data-driven fault detection has been regarded as a 3D image segmentation
task. The models trained from synthetic data are difficult to generalize in
some surveys. Recently, training 3D fault segmentation using sparse manual 2D
slices is thought to yield promising results, but manual labeling has many
false negative labels (abnormal annotations), which is detrimental to training
and consequently to detection performance. Motivated to train 3D fault
segmentation networks under sparse 2D labels while suppressing false negative
labels, we analyze the …
3d annotation arxiv cv image labels network segmentation training