Master Assignment
[B] Disentangled Flows for Anomaly Detection
Type: Master EE/CS/ITC
Period: TBD
Student: (Unassigned)
If you are interested please contact :
Background:
Disentangled Flows for Anomaly Detection
This work will implement disentangled flows with SDFLow, as it separates semantic from visual concepts in latent space. Its focus will be changed from face editing to disentangling representations of other kinds of visual data, such as medical images. Other disentangling networks may also be examined in its place for comparison. The presence of anomalies will be investigated in the disentangled latent spaces. Real-world benchmarking datasets will be used, but first there will be experiments with artificially added noise, for a systematic examination of results.
- Disentangling Physical Parameters for Anomalous Sound Detection Under Domain Shifts https://arxiv.org/abs/2111.0653
- Rare Event Detection using Disentangled Representation Learning https://openaccess.thecvf.com/content_CVPR_2019/papers/Hamaguchi_Rare_Event_Detection_Using_Disentangled_Representation_Learning_CVPR_2019_paper.pdf
- SDFlow https://arxiv.org/pdf/2309.05314.pdf