System and method of robust active learning method using noisy labels and domain adaptation
A system and method is disclosed for labeling an unlabeled dataset, with a labeling budget constraint and noisy oracles (i.e. noisy labels provided by annotator), using a noisy labeled dataset from another domain or application. The system and method combine active learning with noisy labels and active learning with domain adaptation to enhance classification performance.
1. A method that includes an active learning algorithm, comprising:
receiving a noisily labeled source dataset;
applying a robust deep learning algorithm to identify a source classifier for the noisily labeled source dataset;
determining an adapted classifier using a domain discrimination algorithm that operates on the source classifier; and
assigning a label to one or more samples within an unlabeled target dataset based on the active learning algorithm that operably uses a target classifier and the adapted classifier wherein the active learning algorithm is designed using a maximum entropy-based heuristic algorithm.
2. The method of claim 1 , wherein the domain discrimination algorithm is designed as an adversarial network.
3. The method of claim 1 , further comprising: warm-starting the active learning algorithm using the adapted classifier, wherein the active learning algorithm is operable to provide training data for the target classifier.
4. The method of claim 3 , wherein a scheduler controls a duration for warm-starting the active learning algorithm.
5. The method of claim 1 , wherein the robust deep learning algorithm includes a softmax layer followed by a dense linear layer operable to perform denoising of the noisily labeled source dataset.
6. The method of claim 1 , wherein the active learning algorithm is designed using a batch active learning algorithm.
7. The method of claim 6 , wherein the target classifier is updated per-batch.
8. A method that includes an active learning algorithm, comprising:
receiving a noisily labeled source dataset;
applying a robust deep learning algorithm to identify a source classifier for the noisily labeled source dataset;
determining an adapted classifier using an adversarial domain adaptation machine learning algorithm that operates on the source classifier; and
assigning a label to one or more samples within an unlabeled target dataset based on the active learning algorithm that operably uses a target classifier and the adapted classifier.
9. The method of claim 8 , further comprising: warm-starting the active learning algorithm using the adapted classifier, wherein the active learning algorithm is operable to provide training data for the target classifier.
10. The method of claim 9 , wherein a scheduler controls a duration for warm-starting the active learning algorithm.
11. The method of claim 8 , wherein the robust deep learning algorithm includes a softmax layer followed by a dense linear layer operable to perform denoising of the noisily labeled source dataset.
12. A system operable to employ an active learning algorithm, comprising:
a processor operable to:
receive a noisily labeled source dataset;
apply a robust deep learning algorithm to identify a source classifier for the noisily labeled source dataset;
determine an adapted classifier using an domain discrimination machine learning algorithm that operates on the source classifier; and
assign a label to one or more samples within an unlabeled target dataset based on the active learning algorithm that operably uses a target classifier and the adapted classifier wherein the active learning algorithm is designed using a maximum entropy-based heuristic algorithm.
13. The system of claim 12 , wherein the domain discrimination machine learning algorithm is designed using an adversarial network.
14. The system of claim 12 , wherein the processor is further operable to: warm-start the active learning algorithm using the adapted classifier, wherein the active learning algorithm is operable to provide training data for the target classifier.
15. The system of claim 14 , wherein the system further comprises a scheduler that controls a duration to warm-start the active learning algorithm.
16. The system of claim 12 , wherein the robust deep learning algorithm includes a softmax layer followed by a dense linear layer operable to perform denoising of the noisily labeled source dataset.
17. The system of claim 12 , wherein the active learning algorithm is designed using a batch active learning algorithm.
18. The system of claim 17 , wherein the target classifier is updated per-batch.