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Hierarchical Subquery Evaluation for Active Learning on a Graph
Oisin Mac Aodha, Neill Campbell, Jan Kautz, Gabriel Brostow
CVPR 2014
University College London
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Cat
Dog
Horse
Large Image Collections
https://www.flickr.com/photos/cmichel67
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Large Image Collections
https://www.flickr.com/photos/cmichel67
Cat
Dog
Horse
Labeling large image collections is tedious
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Acquiring Annotations
https://www.flickr.com/photos/usnavy https://www.flickr.com/photos/rdecom
Crowdsourcing Specialized Knowledge
Expert time is valuable!
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Active Learning
Oracle
AL Algorithm
User Query
Label
UnlabeledDataset
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Number of user queries
TestAccuracy
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0
Learning Curves
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Number of user queries
1
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Learning Curves
TestAccuracy
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Number of user queries
1
0
Learning Curves
TestAccuracy
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Number of user queries
1
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Learning Curves
TestAccuracy
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Learning Curves
Number of user queries
1
0
TestAccuracy
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Learning Curves
Number of user queries
1
0
We want the largest area under the learning curve
TestAccuracy
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Learning Curves
1
0
TestAccuracy
The number of unlabeled images can be very large!
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Active Learning Wish List
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• Fast updating of classifier for interactive labeling
Active Learning Wish List
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• Fast updating of classifier for interactive labeling• Exploit structure in unlabeled data
Active Learning Wish List
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• Fast updating of classifier for interactive labeling• Exploit structure in unlabeled data• Consistent performance across different datasets
Active Learning Wish List
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• Fast updating of classifier for interactive labeling• Exploit structure in unlabeled data• Consistent performance across different datasets• Make the most of the expert’s time
Active Learning Wish ListGraph Based
Semi-Supervised Learning
Perplexity Graph Construction
Our Hierarchical Subquery Evaluation
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Related Work
Video SegmentationFathi et al. BMVC 2011
Action DetectionBandla and Grauman ICCV 2013
Gaussian Random FieldsZhu et al. ICML 2003
Semantic SegmentationVezhnevets et al. CVPR 2012
RALF: Reinforced Active LearningEbert et al. CVPR 2012
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Image ClassificationKapoor et al. ICCV 2007
…
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xiφ( ) =
Supervised Classification
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xjφ( ) =
Supervised Classification
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Supervised Classification
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Supervised Classification
Decision Boundary
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Semi-supervised learning using Gaussian fields and harmonic functions X. Zhu, Z. Ghahramani, J. LaffertyICML 2003
Fi = P(f(xi) == class1)
wij
Semi-Supervised Learning
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Semi-Supervised Learning
Fi = P(f(xi) == class1)
wij
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Graph Construction
Stochastic neighbor embeddingG. Hinton and S. RoweisNIPS 2002
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Graph Active Learning
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Example 2 Class Graph
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Example 2 Class Graph
Ground Truth
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Example 2 Class GraphActive Learning Strategies
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Active Learning Strategies
• Random
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Active Learning Strategies
• Random• Exploration – clusters
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Active Learning Strategies
• Random• Exploration – clusters• Exploitation – uncertainty
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Active Learning Strategies
• Random• Exploration – clusters• Exploitation – uncertainty
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Active Learning Strategies
• Random• Exploration – clusters• Exploitation – uncertainty• RALF – explore or exploit
Ralf: A reinforced active learning formulation for object class recognitionS. Ebert, M. Fritz, and B. SchieleCVPR 2012
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Active Learning Strategies
• Random• Exploration – clusters• Exploitation – uncertainty• RALF – explore or exploit• Expected Error Reduction – reduce future
error
Toward optimal active learning through sampling estimation of error reductionN. Roy and A. McCallum ICML 2001
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Expected Error Reduction
2 Labeled Points
Ground Truth
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Expected Error Reduction
Current ClassDistribution
Ground Truth
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Expected Error Reduction
Compute the Expected Error (EE) for each unlabled datapoint
Ground Truth
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Expected Error Reduction
? Hypothesize label 1
Ground Truth
Class 1 Class 2
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Expected Error Reduction
? Update model
Ground Truth
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Expected Error Reduction
? Hypothesize label 2
Ground Truth
Class 1 Class 2
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Expected Error Reduction
? Update model
Ground Truth
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Expected Error Reduction
? Compute EE
Ground Truth
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Expected Error Reduction
?
Hypothesize label 1
Ground Truth
Class 1 Class 2
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Expected Error Reduction
?
Update model
Ground Truth
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Expected Error Reduction
?
Hypothesize label 2
Ground Truth
Class 1 Class 2
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Expected Error Reduction
?
Update Model
Ground Truth
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Expected Error Reduction
?
Compute EE
Ground Truth
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Expected Error Reduction
Repeat for all unlabeled
nodes!O(N2)For Zhu et al.
Ground Truth
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Problems with EER
• Need to retrain the classifier with each unlabeled example (subquery) and for each different class label – O(N2)
At each step is it necessary to try every possible subquery?
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Active Learning Strategies
Lower Complexity
Performance RALFCVPR 2012
EERZhu 2003
Random
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Unsupervised Hierarchical Clustering
Unsupervised Hierarchical Clustering
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Authority-shift clustering: Hierarchical clustering by authority seeking on graphsM. Cho and K. Mu LeeCVPR 2010
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Unsupervised Hierarchical Clustering
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…
Unsupervised Hierarchical Clustering
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…
Unsupervised Hierarchical Clustering
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Large clusters (exploration)
Boundary refinement (exploitation) …
Our Hierarchical Subquery Evaluation
After 2 Queries
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Ground Truth
Our Hierarchical Subquery Evaluation
5.6 4.2
3.5After 2 Queries
Best EE
Next nodes to add to the active set
CurrentActive Set
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Ground TruthRemaining Subqueries: 74
Our Hierarchical Subquery Evaluation
Best EE
After 2 Queries
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Ground Truth
6 2.15.6
3.5
4.2
Remaining Subqueries: 2
Our Hierarchical Subquery Evaluation
6 2.1
3.21.1
After 2 Queries
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Ground Truth
5.6
3.5
4.2
Remaining Subqueries: 0
Our Hierarchical Subquery Evaluation
6 2.1After 3 Queries
3.21.1
Label for the example with the best EE is requested
After 2 Queries
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Ground Truth
5.6
3.5
4.2
Remaining Subqueries: 0
Our Hierarchical Subquery Evaluation
After 3 Queries
After 2 Queries
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Ground TruthRemaining Subqueries: 72
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Results
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Results
1579 examples8 classes50 dim BoW PCA
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Results
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Results
Ralf: A reinforced active learning formulation for object class recognitionS. Ebert, M. Fritz, and B. SchieleCVPR 2012
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Results
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13 Different Computer Vision and Machine Learning Datasets
Results - Area Under Learning Curve
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13 Different Computer Vision and Machine Learning Datasets
Results - Area Under Learning Curve
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Summary
• Hierarchical graph based semi-supervised active learning O(N2) -> O(NlogN)
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Summary
• Hierarchical graph based semi-supervised active learning O(N2) -> O(NlogN)
• Robust to dataset type
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Summary
• Hierarchical graph based semi-supervised active learning O(N2) -> O(NlogN)
• Robust to dataset type • Best user query in the time available
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Future Work
• Representation learning – update graph structure during labeling
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• Representation learning – update graph structure during labeling
• Model different annotation costs
Future Work
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• Representation learning – update graph structure during labeling
• Model different annotation costs• Embed new datapoints into the graph
Future Work
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Come visit our poster 01-C-3
http://visual.cs.ucl.ac.uk/pubs/graphActiveLearning
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Graph Construction Comparison
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Timings
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![The iWildCam 2018 Challenge Dataset · Sara Beery, Oisin Mac Aodha, Grant van Horn, Pietro Perona ... and then de-1 arXiv:1904.05986v2 [cs.CV] 24 Apr 2019. ... By test-ing on regions](https://img.pdfslide.us/doc/110x75/5e809aff0645ab21722f6003/the-iwildcam-2018-challenge-dataset-sara-beery-oisin-mac-aodha-grant-van-horn.jpg)
