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Found 6 papers, 1 papers with code
Toward a Geometrical Understanding of Self-supervised Contrastive Learning
When used for transfer learning, the projector is discarded since empirical results show that its representation generalizes more poorly than the encoder's.
Spatial Transformer K-Means
This enables (i) the reduction of intrinsic nuisances associated with the data, reducing the complexity of the clustering task and increasing performances and producing state-of-the-art results, (ii) clustering in the input space of the data, leading to a fully interpretable clustering algorithm, and (iii) the benefit of convergence guarantees.
Interpretable Image Clustering via Diffeomorphism-Aware K-Means
We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds.
A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit
Unfortunately, it is difficult to map their model onto a biologically plausible neural network (NN) with local learning rules.
Manifold-tiling Localized Receptive Fields are Optimal in Similarity-preserving Neural Networks
Many neurons in the brain, such as place cells in the rodent hippocampus, have localized receptive fields, i. e., they respond to a small neighborhood of stimulus space.
Why do similarity matching objectives lead to Hebbian/anti-Hebbian networks?
Modeling self-organization of neural networks for unsupervised learning using Hebbian and anti-Hebbian plasticity has a long history in neuroscience.
