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Found 6 papers, 0 papers with code
Trading robust representations for sample complexity through self-supervised visual experience
Learning in small sample regimes is among the most remarkable features of the human perceptual system.
Discriminate-and-Rectify Encoders: Learning from Image Transformation Sets
We introduce a framework for weakly supervised learning of image embeddings that are robust to transformations and selective to the class distribution, using sets of transforming examples (orbit sets), deep parametrizations and a novel orbit-based loss.
Learning with Group Invariant Features: A Kernel Perspective.
Our analysis bridges invariant feature learning with kernel methods, as we show that this feature map defines an expected Haar-integration kernel that is invariant to the specified group action.
Learning with Group Invariant Features: A Kernel Perspective
Our analysis bridges invariant feature learning with kernel methods, as we show that this feature map defines an expected Haar integration kernel that is invariant to the specified group action.
Learning An Invariant Speech Representation
Recognition of speech, and in particular the ability to generalize and learn from small sets of labelled examples like humans do, depends on an appropriate representation of the acoustic input.
A Deep Representation for Invariance And Music Classification
We present the main theoretical and computational aspects of a framework for unsupervised learning of invariant audio representations, empirically evaluated on music genre classification.
