US 12,079,713 B2
Methods and apparatus for discriminative semantic transfer and physics-inspired optimization of features in deep learning
Anbang Yao, Beijing (CN); Hao Zhao, Beijing (CN); Ming Lu, Beijing (CN); Yiwen Guo, Beijing (CN); and Yurong Chen, Beijing (CN)
Assigned to Intel Corporation, Santa Clara, CA (US)
Filed by Intel Corporation, Santa Clara, CA (US)
Filed on May 3, 2023, as Appl. No. 18/142,997.
Application 18/142,997 is a continuation of application No. 16/609,732, granted, now 11,669,718, previously published as PCT/US2018/033986, filed on May 22, 2018.
Claims priority of provisional application 62/509,960, filed on May 23, 2017.
Claims priority of provisional application 62/509,990, filed on May 23, 2017.
Prior Publication US 2023/0359873 A1, Nov. 9, 2023
This patent is subject to a terminal disclaimer.
Int. Cl. G06V 10/82 (2022.01); G06F 18/214 (2023.01); G06N 3/04 (2023.01); G06N 3/063 (2023.01); G06N 3/08 (2023.01); G06V 10/44 (2022.01); G06V 10/764 (2022.01); G06V 10/94 (2022.01); G06V 20/10 (2022.01); G06V 20/40 (2022.01); G06V 20/70 (2022.01)
CPC G06N 3/063 (2013.01) [G06F 18/214 (2023.01); G06N 3/04 (2013.01); G06N 3/08 (2013.01); G06V 10/454 (2022.01); G06V 10/764 (2022.01); G06V 10/82 (2022.01); G06V 10/955 (2022.01); G06V 20/10 (2022.01); G06V 20/41 (2022.01); G06V 20/70 (2022.01)] 20 Claims
 
1. At least one non-transitory machine-readable medium comprising instructions stored thereon, that if executed by one or more circuitry, cause the one or more circuitry to:
perform a first stage configured to receive a sequence of training images in a convolutional neural network (CNN) to describe objects of a cluttered scene as a semantic segmentation mask;
perform a second stage configured to receive the semantic segmentation mask in a semantic segmentation network and to produce semantic features; and
perform a third stage configured to use weights from the first stage as feature extractors and weights from the second stage as classifiers in order to identify at least one partially occluded edge of the cluttered scene using the semantic features.