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Found 7 papers, 6 papers with code
Manifold Contrastive Learning with Variational Lie Group Operators
In this work, we propose a contrastive learning approach that directly models the latent manifold using Lie group operators parameterized by coefficients with a sparsity-promoting prior.
PrefGen: Preference Guided Image Generation with Relative Attributes
Using information from a sequence of query responses, we can estimate user preferences over a set of image attributes and perform preference-guided image editing and generation.
Active Learning of Ordinal Embeddings: A User Study on Football Data
We adapt an entropy-based active learning method with recent work from triplet mining to collect easy-to-answer but still informative annotations from human participants and use them to train a deep convolutional network that generalizes to unseen samples.
Variational Sparse Coding with Learned Thresholding
Sparse coding strategies have been lauded for their parsimonious representations of data that leverage low dimensional structure.
Oracle Guided Image Synthesis with Relative Queries
Isolating and controlling specific features in the outputs of generative models in a user-friendly way is a difficult and open-ended problem.
Learning Identity-Preserving Transformations on Data Manifolds
However, these approaches are limited because they require transformation labels when training their models and they lack a method for determining which regions of the manifold are appropriate for applying each specific operator.
Learning sparse codes from compressed representations with biologically plausible local wiring constraints
Unfortunately, current proposals for sparse coding in the compressed space require a centralized compression process (i. e., dense random matrix) that is biologically unrealistic due to local wiring constraints observed in neural circuits.
