The current autoencoder and VAE only supports 2-d data, while there is a need to handle 3-d images. PR welcomed!
Contributor guide
Tech stack
python
Domain
machine learning
Issue type
feature
DifficultyEstimated implementation difficulty for a new contributor, from 1 for very small changes to 5 for expert-level work.
3
Estimated timeA rough time range for an experienced contributor to investigate, implement, test, and prepare a pull request.
1-2 days
Activity statusHow available the issue appears right now: fresh, active, stale, blocked, or waiting on maintainer input.
stale
ClarityHow clearly the issue explains the expected change, acceptance criteria, and next step.
clear
Prerequisites
PythonDeep learning basicsConvolutional neural networksFamiliarity with pyod library
Newbie friendlinessA 1-100 score estimating how approachable this issue is for first-time contributors.
60
Research direction
The issue requests adding a CNN based autoencoder for 3D image outlier detection. Start by examining the existing autoencoder and VAE implementations in pyod (e.g., in pyod/models/autoencoder.py). Study how 2D data is handled and extend to 3D using convolutional layers (e.g., Conv2D to Conv3D). Consider using PyTorch or TensorFlow, depending on the library's current backend. Look at any related discussions in the comments. The implementation should match the library's API and include tests.
