1. We use model-inferred embeddings. Say, for the instance segmentation task, we use deep learning networks to transform the input image into a dense embeddings representation, on top of which we run clustering and density estimation to find if the given embedding/image/feature combination is an outlier (or belongs to low-density region)
2. We allow users to define custom signals to identify edge-cases, specific to their use-case. A very simple example could be calculate brightness or Hue properties on the input image and see if that is an outlier compared to the training distribution.
1. We use model-inferred embeddings. Say, for the instance segmentation task, we use deep learning networks to transform the input image into a dense embeddings representation, on top of which we run clustering and density estimation to find if the given embedding/image/feature combination is an outlier (or belongs to low-density region)
2. We allow users to define custom signals to identify edge-cases, specific to their use-case. A very simple example could be calculate brightness or Hue properties on the input image and see if that is an outlier compared to the training distribution.