Patent application EP17198775A (EP), filed 2017-10-27
Publication number: EP3477553A1
Title: METHOD FOR DETECTING AN ANOMALOUS IMAGE AMONG A FIRST DATASET OF IMAGES USING AN ADVERSARIAL AUTOENCODER
Abstract: [0001]    A method for detecting an anomalous image among a first dataset of images using an Adversarial Autoencoder (12, 14, 16) is introduced. The Adversarial Autoencoder (12, 14, 16) is trained in a first training with a training dataset of images (11), whereas the Adversarial Autoencoder (12, 14, 16) is optimized such that a distribution of latent representations (13) of images of the training dataset of images (11) approaches a predetermined prior distribution (17) and that a reconstruction error of reconstructed images (15) of the training dataset of images is minimized. After the first training of the Adversarial Autoencoder (12, 14, 16), anomalies are detected in the latent representation (13) and the Adversarial Autoencoder (12, 14, 16) is trained in a second training with the training dataset of images (11), but taking into account the detected anomalies. The anomalous image among the first dataset of images is detected by the trained Adversarial Autoencoder (12, 14, 16) dependent on at least one of the reconstruction error of the image and the probability density under the predetermined prior distribution (17).
IPC codes: G06N   3/    04            A I
G06N   3/    08            A I
G06N   5/    04            A I
G06V  10/   764            A I
G06N   3/  0455            A I
G06N   3/   088            A I
G06N   3/  0895            A I
G06V  10/    82            A I
Claims: number: 1
text: Method for detecting an anomalous image among a first dataset of images using an Adversarial Autoencoder (12, 14, 16) characterized in that
number: 2
text: Method according to claim 1, characterized in that the detection of the anomalies in the latent representation (13) is done dependent on an expected anomaly rate.
number: 3
text: Method according to one of the preceding claims, characterized in that the detection of the anomalies in the latent representation (13) is done dependent on at least one of their reconstruction error and their probability density under the predetermined prior distribution (17).
number: 4
text: Method according to one of the preceding claims, characterized in that the detection of the anomalies in the latent representation (13) is done using a one-class support vector machine or a local outlier factor algorithm.
number: 5
text: Method according to one of the preceding claims, characterized in that anomalies detected in the latent representation (13) are taken into account by excluding detected anomalies from the training set (11) for the second training.
number: 6
text: Method according to one of the preceding claims, characterized in that anomalies detected in the latent representation (13) are taken into account by using a weighted loss function for tuning the Adversarial Autoencoder (12, 14, 16) in at least one subsequent training, whereas anomalies detected in the latent representation (13) receive a reduced weight in the at least one subsequent training.
number: 7
text: Method according to one of the preceding claims, characterized in that the anomalies detected in the latent representation (13) are taken into account by modifying a reconstruction target for each of a set of the anomalies detected in the latent representation (13) to a noise-corrupted version of itself for at least one subsequent training.
number: 8
text: Method according to one of the preceding claims, characterized in that the anomalies detected in the latent representation (13) are taken into account by modifying a reconstruction target for each of a set of the anomalies detected in the latent representation (13) to an image close or closest in the latent space that is identified as no anomaly in at least one subsequent training.
number: 9
text: Method according to one of the preceding claims, characterized in that the first training and the second training are repeatedly conducted consecutively for a certain number of repetitions or until a training target is reached.
number: 10
text: Method according to one of the preceding claims, characterized in that the first training comprises a first predetermined number of iterations.
number: 11
text: Method according to one of the preceding claims, characterized in that the second training comprises a second predetermined number of iterations.
number: 12
text: Method according to one of the preceding claims, characterized in that the predetermined prior distribution includes at least one dedicated rejection class.
number: 13
text: Method according to one of the preceding claims, characterized in that images of the training dataset of images (11) are initially unlabeled.
number: 14
text: Method according to one of the preceding claims, characterized in that the method includes a query to a user to identify a presented image as normal or anomaly.
number: 15
text: Method according to claim 14, characterized in that the detected anomalies in the latent representation (13) taken into account in the second training include the user-identified anomalies.
number: 16
text: Method according to claim 14 or claim 15, characterized in that the detection of the anomalies in the latent representation (13) is done using a one-class support vector machine or a local outlier factor algorithm and whereas for a training of the one-class Support vector machine or the local outlier factor algorithm images of the training dataset of images (11), which are identified by the user as normal or as anomalies, are excluded.
number: 17
text: Method according to one of the preceding claims, characterized in that an anomaly score for images in the first dataset of images is output.
number: 18
text: Method according to one of the preceding claims, characterized in that an anomaly score for images in the training dataset (11) of images is output.
number: 19
text: Method according to claim 14, characterized in that images presented to the user are selected dependent on their anomaly score.
number: 20
text: Method according to one of claims 17 to 19, characterized in that the anomaly score is calculated based on at least one of a reconstruction error and a probability density under the predetermined prior distribution (17).
number: 21
text: Method according to one of the preceding claims, characterized in that it is used in visual quality inspection, medical image analysis, visual surveillance, or automated driving.
number: 22
text: Computer program, adapted to carry out a method according to one of claims 1 to 20.
number: 23
text: Computing device, adapted to carry out a method according to one of claims 1 to 20.
Office action: search_report, dated 2019-05-01
Rejection reasons: inventive_step_epc_56
Prior art cited: ref_id: - MAKHZANI, A.; SHLENS, J.; JAITLY, N.; GOODFELLOW, I.; FREY, B.: "Adversarial autoencoders", ARXIV PREPRINT ARXIV:1511.05644, 2015
ref_type: npl
severity: background
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ref_type: npl
severity: background
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ref_type: npl
severity: background
ref_id: - VALENTIN  LEVEAU ET AL: "ADVERSARIAL AUTOENCODERS FOR NOVELTY DETECTION", 21 February 2017 (2017-02-21), XP055469416, Retrieved from the Internet <URL:https://openreview.net/pdf?id=SyuncaEKx> [retrieved on 20180423]
ref_type: npl
severity: background
ref_id: - XIA YAN ET AL: "Learning Discriminative Reconstructions for Unsupervised Outlier Removal", 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), IEEE, 7 December 2015 (2015-12-07), pages 1511 - 1519, XP032866499, DOI: 10.1109/ICCV.2015.177
ref_type: npl
severity: background
ref_id: - ERFANI SARAH M ET AL: "High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning", PATTERN RECOGNITION, ELSEVIER, GB, vol. 58, 14 April 2016 (2016-04-14), pages 121 - 134, XP029553786, ISSN: 0031-3203, DOI: 10.1016/J.PATCOG.2016.03.028
ref_type: npl
severity: background
ref_id: - Valentin Leveau ET AL, "ADVERSARIAL AUTOENCODERS FOR NOVELTY DETECTION", (20170221), URL: https://openreview.net/pdf?id=SyuncaEKx, (20180423), XP055469416 [I] 1-5,7-23 * Abstract; Sections I-III * [Y] 6
ref_type: npl
severity: obviousness
ref_id: - XIA YAN ET AL, "Learning Discriminative Reconstructions for Unsupervised Outlier Removal", 2015 IEEE INTERNATIONAL CONFERENCE ON COMPUTER VISION (ICCV), IEEE, (20151207), doi:10.1109/ICCV.2015.177, pages 1511 - 1519, XP032866499 [Y] 6 * Abstract; Sections I, III, IV, VI;; figures 5, 7 * [A] 1-5,7-23
ref_type: npl
severity: obviousness
ref_id: - ERFANI SARAH M ET AL, "High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning", PATTERN RECOGNITION, ELSEVIER, GB, (20160414), vol. 58, doi:10.1016/J.PATCOG.2016.03.028, ISSN 0031-3203, pages 121 - 134, XP029553786 [A] 1-23 * Abstract; Sections 1, 3, 4.1, 5;; figure 1 *
ref_type: npl
severity: background
Final disposition: pending (2019-05-01)