Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Added API ood post-training ood classifiers #119

Merged
merged 4 commits into from
Aug 24, 2023
Merged
Show file tree
Hide file tree
Changes from all commits
Commits
File filter

Filter by extension

Filter by extension

Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
10 changes: 10 additions & 0 deletions docs/source/references/ood_classifier.rst
Original file line number Diff line number Diff line change
@@ -0,0 +1,10 @@
.. _ood_detection:

Out-Of-Distribution (OOD) detection
==================
Starting from a trained a neural classifier, it's possible to fit one of the models below
to help distinguish between in-distribution and out of distribution inputs.

.. autoclass:: fortuna.ood_detection.mahalanobis.MalahanobisOODClassifier

.. autoclass:: fortuna.ood_detection.ddu.DeepDeterministicUncertaintyOODClassifier
1 change: 1 addition & 0 deletions docs/source/references/references.rst
Original file line number Diff line number Diff line change
Expand Up @@ -12,6 +12,7 @@ API References
output_calibrator
prob_output_layer
conformal
ood_detection
data_loader
metric
utils
Expand Down
2 changes: 1 addition & 1 deletion examples/index.rst
Original file line number Diff line number Diff line change
Expand Up @@ -16,8 +16,8 @@ In this section we show some examples of how to use Fortuna in classification an
multivalid_coverage
sinusoidal_regression
two_moons_classification
two_moons_classification_ood
subnet_calibration
two_moons_classification_sngp
scaling_up_bayesian_inference
mnist_classification_sghmc
sgmcmc_diagnostics
Expand Down
Loading