Supervision from Context
Examples
Ideas & Goals
- Learn word representations from context in sentence
- Allow accurate syntactic & semantic arithmetic [1]
- Create comprehensive test set for regularities
Semantic & Syntactic Relations in Test
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Proposed Architectures
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- CBOW = Continuous Bag-of-Words
- CBOW shares projection matrices
- Words are one-hot vocabulary vectors
Results
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Ideas & Goals
- Leverage intra-image correlations for learning
- Improve "unsupervised" pre-training
Context Classification
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- Predict relative position of second patch
- Random variation and offset to not learn trivial relations
Architecture
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Nearest-Neighbour Examples
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- Representation from fc6 layer
- NN via normalized cross-correlation
Results (VOC-2007)
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Findings
- Not as good as supervised pre-training (ImageNet)
- Better than existing unsupervised pre-training
- Representation still not ideal (chromatic aberration)
Transformation Invariance
Example
Ideas & Goals
- Use images from different angles to predict camera motion
- Find representation useful for higher level tasks
SF and KITTI dataset examples
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MNIST Results: Random Transformations
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- Random translation & rotation $\rightarrow$ classify transformation
- Simple siamese network w/ varying base net
ILSVRC-12 Top-5 Accuracy
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- Base architecture similar to AlexNet
- Also good results for keypoint matching & odometry
- (1, 5, 10, 20) examples per class finetuning
Environment Exploration
Example
