Deep learning-based anomaly detection in images

1. A system comprising:

at least one hardware processor; and

a non-transitory computer-readable storage medium having stored thereon program instruction, the program instructions executable by the at least one hardware processor to:

receive, as input, training images, wherein at least a majority of said training images represent normal data instances,

receive, as input, a target image,

extract (i) a set of feature representations from a plurality of sub-image locations within each of said training images, and (ii) target feature representations from a plurality of target sub-image locations within said target image,

calculate, with respect to a target sub-image location of said plurality of target sub-image locations in said target image, a distance between (iii) said target feature representation of said target sub-image location, and (iv) a subset from said set of feature representations comprising the k nearest said feature representations to said target feature representation, and

determine that said target sub-image location is anomalous, when said calculated distance exceeds a predetermined threshold.

2. The system of claim 1 , wherein said program instructions are further executable to perform said calculating and said determining with respect to all of said plurality of target sub-image locations.

3. The system of claim 2 , wherein said program instructions are further executable to designate a segment of said target image as comprising anomalous target sub-image locations, based, at least in part, on said determining.

4. The system of claim 1 , wherein said program instructions are further executable to apply a clustering algorithm to said set of feature representations, to obtain clusters of said feature representations, wherein said calculating comprises calculating, with respect to a target sub-image location of said plurality of target sub-image locations, a distance between (i) said target feature representation of said target sub-image location, and (ii) the k nearest means of said clusters to said target feature representation.

5. The system of claim 1 , wherein said extracting is performed by applying a pre-trained machine learning model to said training images and said target image, wherein said pre-trained machine learning model comprises a deep-learning neural network architecture comprising a feature pyramid having a plurality of layers, wherein each of said plurality of layers has a specified resolution, and wherein said extracting comprises concatenating features from two or more of said plurality of layers.

6. The system of claim 1 , wherein said calculating comprises:

(i) selecting, from said training images, a specified number n of nearest images to said target image; and

(ii) calculating, with respect to a target sub-image location of said plurality of target sub-image locations in said target image, a distance between (a) said target feature representation of said target sub-image location, and (b) said feature representations from all of said sub-image locations in said n nearest images; and

(iii) determining that said target sub-image location is anomalous, when said calculated distance exceeds a predetermined threshold.

7. The system of claim 1 , wherein each of said sub-image locations represents one pixel in (i) each of said training images, and (ii) said target image.

8. A computer-implemented method comprising:

receiving, as input, training images, wherein at least a majority of said training images represent normal data instances;

receiving, as input, a target image;

extracting (i) a set of feature representations from a plurality of sub-image locations within each of said training images, and (ii) target feature representations from a plurality of target sub-image locations within said target image;

calculating, with respect to a target sub-image location of said plurality of target sub-image locations in said target image, a distance between (iii) said target feature representation of said target sub-image location, and (iv) a subset from said set of feature representations comprising the k nearest said feature representations to said target feature representation; and

determining that said target sub-image location is anomalous, when said calculated distance exceeds a predetermined threshold.

9. The computer-implemented method of claim 8 , further comprising performing said calculating and said determining with respect to all of said plurality of target sub-image locations.

10. The computer-implemented method of claim 9 , further comprising designating a segment of said target image as comprising anomalous target sub-image locations, based, at least in part, on said determining.

11. The computer-implemented method of claim 8 , further comprising applying a clustering algorithm to said set of feature representations, to obtain clusters of said feature representations, wherein said calculating comprises calculating, with respect to a target sub-image location of said plurality of target sub-image locations, a distance between (i) said target feature representation of said target sub-image location, and (ii) the k nearest means of said clusters to said target feature representation.

12. The computer-implemented method of claim 8 , wherein said extracting is performed by applying a pre-trained machine learning model to said training images and said target image, wherein said pre-trained machine learning model comprises a deep-learning neural network architecture comprising a feature pyramid having a plurality of layers, wherein each of said plurality of layers has a specified resolution, and wherein said extracting comprises concatenating features from two or more of said plurality of layers.

13. The computer-implemented method of claim 8 , wherein said calculating comprises:

(i) selecting, from said training images, a specified number n of nearest images to said target image; and

(ii) calculating, with respect to a target sub-image location of said plurality of target sub-image locations in said target image, a distance between (a) said target feature representation of said target sub-image location, and (b) said feature representations from all of said sub-image locations in said n nearest images; and

(iii) determining that said target sub-image location is anomalous, when said calculated distance exceeds a predetermined threshold.

14. The computer-implemented method of claim 8 , wherein each of said sub-image locations represents one pixel in (i) each of said training images, and (ii) said target image.

15. A computer program product comprising a non-transitory computer-readable storage medium having program instructions embodied therewith, the program instructions executable by at least one hardware processor to comprising:

receive, as input, training images, wherein at least a majority of said training images represent normal data instances;

receive, as input, a target image;

extract (i) a set of feature representations from a plurality of sub-image locations within each of said training images, and (ii) target feature representations from a plurality of target sub-image locations within said target image;

calculate, with respect to a target sub-image location of said plurality of target sub-image locations in said target image, a distance between (iii) said target feature representation of said target sub-image location, and (iv) a subset from said set of feature representations comprising the k nearest said feature representations to said target feature representation; and

determine that said target sub-image location is anomalous, when said calculated distance exceeds a predetermined threshold.

16. The computer program product of claim 15 , wherein said program instructions are further executable to perform said calculating and said determining with respect to all of said plurality of target sub-image locations.

17. The computer program product of claim 16 , wherein said program instructions are further executable to designate a segment of said target sub-image as comprising anomalous target image locations, based, at least in part, on said determining.

18. The computer program product of claim 15 , wherein said program instructions are further executable to apply a clustering algorithm to said set of feature representations, to obtain clusters of said feature representations, wherein said calculating comprises calculating, with respect to a target sub-image location of said plurality of target sub-image locations, a distance between (i) said target feature representation of said target sub-image location, and (ii) the k nearest means of said clusters to said target feature representation.

19. The computer program product of claim 15 , wherein said extracting is performed by applying a pre-trained machine learning model to said training images and said target image, wherein said pre-trained machine learning model comprises a deep-learning neural network architecture comprising a feature pyramid having a plurality of layers, wherein each of said plurality of layers has a specified resolution, and wherein said extracting comprises concatenating features from two or more of said plurality of layers.

20. The computer program product of claim 15 , wherein said calculating comprises:

(i) selecting, from said training images, a specified number n of nearest images to said target image; and

(ii) calculating, with respect to a target sub-image location of said plurality of target sub-image locations in said target image, a distance between (a) said target feature representation of said target sub-image location, and (b) said feature representations from all of said sub-image locations in said n nearest images; and

(iii) determining that said target sub-image location is anomalous, when said calculated distance exceeds a predetermined threshold.