SYSTEM AND METHOD FOR SELECTING UNLABLED DATA FOR BUILDING LEARNING MACHINES
Systems and methods for selecting unlabeled data for building and improving the performance of a learning machine are disclosed. In an aspect, such a system may include a reference learning machine, a set of labeled data, and a learning machine analyzer. The learning machine analyzer is configured to receive the reference learning machine and the set of labeled data as inputs and analyze the inner working of the reference learning machine to produce a selected set of unlabeled data. In an aspect, the learning machine analyzer identifies and measures a relation between different input data samples and finds all pairwise relations to construct a relational graph. In an aspect, the relational graph visualizes how much the different input data samples are like each other in higher dimensions inside the reference learning machine.
1 . A system for selecting unlabeled data for building and improving performance of a learning machine, comprising:
a reference learning machine;
a set of labeled data; and
a learning machine analyzer that:
receives the reference learning machine and the set of labeled data as input data samples, and
analyzes an inner working of the reference learning machine to produce a selected set of unlabeled data.
2 . The system of claim 1 , wherein the learning machine analyzer identifies and measures a relation between different input data samples of the set of labeled data and finds pairwise relations to construct a relational graph.
3 . The system of claim 2 , wherein the relational graph provides a visualization of how much the different input data samples are similar to each other in higher dimensions inside the reference learning machine.
4 . The system of claim 1 , wherein one or more first activation vectors extracted from the reference learning machine are processed and projected to a second vector which is designed to highlight similarities between the input data samples.
5 . The system of claim 4 , wherein the second vector has a much lower dimension compared to the one or more first activation vectors.
6 . The system of claim 1 , further comprises a data annotator to automatically annotate the selected set of unlabeled data.
7 . A method for selecting unlabeled data for building and improving performance of a learning machine, the method comprising:
receiving a reference learning machine;
receiving a set of labeled data as input data samples; and
analyzing an inner working of the reference learning machine to produce a selected set of unlabeled data.
8 . The method of claim 7 , further comprising identifying and measuring a relation between different input data samples of the set of labeled data and finding pairwise relations to construct a relational graph.
9 . The method of claim 8 , further comprising providing a visualization of how much the different input data samples are similar to each other in higher dimensions inside the reference learning machine.
10 . The method of claim 7 , wherein one or more first activation vectors extracted from the reference learning machine are processed and projected to a second vector which is designed to highlight similarities between the input data samples.
11 . The method of claim 10 , wherein the second vector has a much lower dimension compared to the one or more first activation vectors.
12 . The method of claim 7 , further comprising automatically annotate the selected set of unlabeled data.
13 . A non-transitory computer-readable medium storing instructions, executable by a processor, the instructions comprising instructions for:
receiving a reference learning machine;
receiving a set of labeled data as input data samples; and
analyzing an inner working of the reference learning machine to produce a selected set of unlabeled data.
14 . The non-transitory computer-readable medium of claim 13 , further including instructions for identifying and measuring a relation between different input data samples of the set of labeled data and finding pairwise relations to construct a relational graph.
15 . The non-transitory computer-readable medium of claim 14 , wherein the relational graph provides a visualization of how much the different input data samples are similar to each other in higher dimensions inside the reference learning machine.
16 . The non-transitory computer-readable medium of claim 13 , wherein one or more first activation vectors extracted from the reference learning machine are processed and projected to a second vector which is designed to highlight similarities between the input data samples.
17 . The non-transitory computer-readable medium of claim 16 , wherein the second vector has a much lower dimension compared to the one or more first activation vectors.
18 . The non-transitory computer-readable medium of claim 13 , further comprising automatically annotate the selected set of unlabeled data.