Granted Patent B2

US 11,663,243 · App. 17/161,541 · Granted May 30, 2023

Inventors: Vinicius Michel Gottin (Rio de Janeiro, BR); Tiago Salviano Calmon (Rio de Janeiro, BR); Paulo Abelha Ferreira (Rio de Janeiro, BR)

Assignee: EMC IP Holding Company LLC

G06F16/285

Abstract

Claims (121)

1. An information handling system for managing detection of objects, comprising:

storage for storing:

an encoder;

a critical class classifier;

a general classifier; and

a decoder;

a processor programmed to:

obtain data that may comprise one or more of the objects;

encode the data using the encoder to obtain encoded data;

obtain a critical class classification for the encoded data using the critical class classifier;

obtain a general classification for the encoded data using the general classifier;

condition the encoded data to obtain conditioned encoded data;

decode the conditioned encoded data using the decoder to obtain reconstructed data;

make a determination that the reconstructed data and the critical class classification indicate that the data is an unknown classification;

classify the data as being an unknown classification based on the determination; and

perform an action set based on the unknown classification of the data.

2. The information handling system of claim 1 , wherein making the determination comprises:

obtaining a reconstruction error threshold for the decoder;

increasing the reconstruction error threshold based on the critical class classification indicating that the data is a member of a critical class detected by the critical class classifier to obtain a modified reconstruction error threshold;

making an identification that reconstruction error of the reconstructed data is larger than the modification reconstruction error threshold; and

making the determination based on the identification.

3. The information handling system of claim 1 , wherein making the determination comprises:

obtaining a reconstruction error threshold for the decoder;

making a first identification that the critical class classification indicates that the data is not a member of a critical class detected by the critical class classifier;

making a second identification that reconstruction error of the reconstructed data is larger than the reconstruction error threshold; and

making the determination based on the second identification.

4. The information handling system of claim 1 , wherein the processor is further programmed to:

obtain second data that may comprise the one or more of the objects;

encode the second data using the encoder to obtain second encoded data;

obtain a second critical class classification for the encoded second data using the critical class classifier;

obtain a second general classification for the encoded second data using the general classifier;

condition the encoded second data to obtain conditioned encoded second data;

decode the conditioned encoded second data to obtain second reconstructed data;

make a second determination that the second reconstructed data and the second critical class classification indicate that the second data is a known classification;

classify the data as being the second general classification based on the second determination; and

perform a second action set based on the second general classification of the second data.

5. The information handling system of claim 4 , wherein making the second determination comprises:

obtaining a reconstruction error threshold for the decoder;

increasing the reconstruction error threshold based on the second critical class classification indicating that the second data is a member of a critical class detected by the critical class classifier to obtain a modified reconstruction error threshold;

making an identification that reconstruction error of the second reconstructed data is larger than the modification reconstruction error threshold; and

making the second determination based on the identification.

6. The information handling system of claim 4 , wherein making the second determination comprises:

obtaining a reconstruction error threshold for the decoder;

making a first identification that the second critical class classification indicates that the second data is not a member of a critical class which the critical class classifier is trained to detect;

making a second identification that reconstruction error of the second reconstructed data is larger than the reconstruction error threshold; and

making the determination based on the second identification.

7. The information handling system of claim 1 , wherein the critical class classification for the encoded data is obtained by using the encoded data as input for the critical class classifier.

8. The information handling system of claim 1 , wherein the general classification for the encoded data is obtained by using the encoded data as input for the general classifier.

9. The information handling system of claim 1 , wherein conditioning the encoded data reduces a likelihood that reconstructed data will match the data when the data is dissimilar to training data used to obtain the decoder.

10. The information handling system of claim 1 , wherein the action set comprises:

providing the unknown classification of the data to an application that requested classification of the data.

11. A method for managing detection of objects, comprising:

obtaining data that may comprise one or more of the objects;

encoding the data using an encoder to obtain encoded data;

obtaining a critical class classification for the encoded data using a critical class classifier;

obtaining a general classification for the encoded data using a general classifier;

conditioning the encoded data to obtain conditioned encoded data;

decoding the conditioned encoded data using a decoder to obtain reconstructed data;

making a determination that the reconstructed data and the critical class classification indicate that the data is an unknown classification;

classifying the data as being an unknown classification based on the determination; and

performing an action set based on the unknown classification of the data.

12. The method of claim 11 , wherein making the determination comprises:

obtaining a reconstruction error threshold for the decoder;

increasing the reconstruction error threshold based on the critical class classification indicating that the data is a member of a critical class detected by the critical class classifier to obtain a modified reconstruction error threshold;

making an identification that reconstruction error of the reconstructed data is larger than the modification reconstruction error threshold; and

making the determination based on the identification.

13. The method of claim 11 , wherein making the determination comprises:

obtaining a reconstruction error threshold for the decoder;

making a first identification that the critical class classification indicates that the data is not a member of a critical class detected by the critical class classifier;

making a second identification that reconstruction error of the reconstructed data is larger than the reconstruction error threshold; and

making the determination based on the second identification.

14. The method of claim 11 , further comprising:

obtaining second data that may comprise the one or more of the objects;

encoding the second data using the encoder to obtain second encoded data;

obtaining a second critical class classification for the encoded second data using the critical class classifier;

obtaining a second general classification for the encoded second data using the general classifier;

conditioning the encoded second data to obtain conditioned encoded second data;

decoding the conditioned encoded second data using the decoder to obtain second reconstructed data;

making a second determination that the second reconstructed data and the second critical class classification indicate that the second data is a known classification;

classifying the data as being the second general classification based on the second determination; and

performing a second action set based on the second general classification of the second data.

15. The method of claim 14 , wherein making the second determination comprises:

obtaining a reconstruction error threshold for the decoder;

increasing the reconstruction error threshold based on the second critical class classification indicating that the second data is a member of a critical class detected by the critical class classifier to obtain a modified reconstruction error threshold;

making an identification that reconstruction error of the second reconstructed data is larger than the modification reconstruction error threshold; and

making the second determination based on the identification.

16. A non-transitory computer readable medium comprising computer readable program code, which when executed by a computer processor enables the computer processor to perform a method for managing detection of objects, the method comprising:

obtaining data that may comprise one or more of the objects;

encoding the data using an encoder to obtain encoded data;

obtaining a critical class classification for the encoded data using a critical class classifier;

obtaining a general classification for the encoded data using a general classifier;

conditioning the encoded data to obtain conditioned encoded data;

decoding the conditioned encoded data using a decoder to obtain reconstructed data;

making a determination that the reconstructed data and the critical class classification indicate that the data is an unknown classification;

classifying the data as being an unknown classification based on the determination; and

performing an action set based on the unknown classification of the data.

17. The non-transitory computer readable medium of claim 16 , wherein making the determination comprises:

obtaining a reconstruction error threshold for the decoder;

increasing the reconstruction error threshold based on the critical class classification indicating that the data is a member of a critical class detected by the critical class classifier to obtain a modified reconstruction error threshold;

making an identification that reconstruction error of the reconstructed data is larger than the modification reconstruction error threshold; and

making the determination based on the identification.

18. The non-transitory computer readable medium of claim 16 , wherein making the determination comprises:

obtaining a reconstruction error threshold for the decoder;

making a first identification that the critical class classification indicates that the data is not a member of a critical class detected by the critical class classifier;

making a second identification that reconstruction error of the reconstructed data is larger than the reconstruction error threshold; and

making the determination based on the second identification.

19. The non-transitory computer readable medium of claim 16 , wherein the method further comprises:

obtaining second data that may comprise the one or more of the objects;

encoding the second data using the encoder to obtain second encoded data;

obtaining a second critical class classification for the encoded second data using the critical class classifier;

obtaining a second general classification for the encoded second data using the general classifier;

conditioning the encoded second data to obtain conditioned encoded second data;

decoding the conditioned encoded second data using the decoder to obtain second reconstructed data;

making a second determination that the second reconstructed data and the second critical class classification indicate that the second data is a known classification;

classifying the data as being the second general classification based on the second determination; and

performing a second action set based on the second general classification of the second data.

20. The non-transitory computer readable medium of claim 19 , wherein making the second determination comprises:

obtaining a reconstruction error threshold for the decoder;

increasing the reconstruction error threshold based on the second critical class classification indicating that the second data is a member of a critical class detected by the critical class classifier to obtain a modified reconstruction error threshold;

making an identification that reconstruction error of the second reconstructed data is larger than the modification reconstruction error threshold; and

making the second determination based on the identification.

Assignments (9)

Continuity (1)