The current autoencoder and VAE only supports 2-d data, while there is a need to handle 3-d images. PR welcomed!
贡献者指南
技术栈
python
领域
machine learning
议题类型
feature
难度面向新贡献者的预计实现难度，1 表示很小改动，5 表示专家级工作。
3
预计时间有经验贡献者完成调查、实现、测试并准备 pull request 的粗略时间范围。
1-2 days
活动状态议题当前的可参与程度：新鲜、活跃、陈旧、阻塞或等待维护者输入。
stale
清晰度议题是否清楚说明期望改动、验收标准和下一步。
clear
前置要求
PythonDeep learning basicsConvolutional neural networksFamiliarity with pyod library
新手友好度1-100 的估计分数，表示该议题对首次贡献者的友好程度。
60
研究方向
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.
