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make CTD `GenerativeSampler` visibility-aware by deruyter92 · Pull Request #3305 · DeepLabCut/DeepLabCut · GitHub
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make CTD GenerativeSampler visibility-aware #3305

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deruyter92 merged 2 commits into from
May 12, 2026
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make CTD GenerativeSampler visibility-aware #3305

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9663286
make GenerativeSampler visibility-aware
deruyter92 May 4, 2026
d48f369
Merge branch 'main' into jaap/update_ctd_generative_sampler
C-Achard May 12, 2026
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make GenerativeSampler visibility-aware
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@deruyter92
deruyter92 committed May 4, 2026
commit 9663286aab8f96df431df5a19a3d12ac42476e0d
8 changes: 8 additions & 0 deletions deeplabcut/pose_estimation_pytorch/data/generative_sampling.py
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Expand Up @@ -173,6 +173,14 @@ def __call__(

N = 500 # TODO: do not know how this is set
for j in range(self.num_keypoints):
# Skip unlabeled / invisible GT joints. Synthesizing a conditional
# keypoint for them creates a spurious cue and biases CTD training.
# Previously, this was prevented implicitly by NaN propagation; now
# we make the contract explicit. (Required since PR #2995)
if keypoints[j, 2] <= 0:
synth_joints[j] = 0 # (x, y, vis) = (0, 0, 0)
continue

# source keypoint position candidates to generate error on that (gt, swap, inv, swap+inv)
coord_list = []
# on top of gt
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