Extended Data Fig. 3: PCA-derived losses drive most improvements in semi-supervised models. | Nature Methods

Extended Data Fig. 3: PCA-derived losses drive most improvements in semi-supervised models.

From: Lightning Pose: improved animal pose estimation via semi-supervised learning, Bayesian ensembling and cloud-native open-source tools

Extended Data Fig. 3: PCA-derived losses drive most improvements in semi-supervised models.

For each model type we train three networks with different random seeds controlling the data presentation order. The models train on 75 labeled frames and unlabeled videos. We plot the mean pixel error and 95% CI across keypoints and OOD frames, as a function of ensemble standard deviation, as in Fig. 4. At the 100% vertical line, n=17150 keypoints for mirror-mouse, n=18180 for mirror-fish, and n=89180 for CRIM13.

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