Supplementary Figure 2: Cluster sampling to promote pose diversity in the labeling dataset.
From: Fast animal pose estimation using deep neural networks
a, PCA of unlabeled images captures 80% of the variance in the data (gray line; n = 29,500 images) within 50 components (blue bars). b, Top PCA eigenmodes visualized as coefficient images. Red and blue shading denote positive and negative coefficients at each pixel, respectively. Areas of similar colors indicate correlated pixel intensities within a given mode. c, Cluster centroids identified by k-means after PCA. Red and blue shading denote pixels with higher or lower intensity than the overall mean, respectively.
