Metric Learning

AGCN is a novel spectral graph convolution network that feed on original data of diverse graph structures. Image credit: Adaptive Graph Convolutional Neural Networks

DeCLUTR is an approach for learning universal sentence embeddings that utilizes a self-supervised objective that does not require labelled training data. The objective learns universal sentence embeddings by training an encoder to minimize the distance between the embeddings of textual segments randomly sampled from nearby in the same document.

Geomancer is a nonparametric algorithm for symmetry-based disentangling of data manifolds. It learns a set of subspaces to assign to each point in the dataset, where each subspace is the tangent space of one disentangled submanifold. This means that geomancer can be used to disentangle manifolds for which there may not be a global axis-aligned coordinate system.

A generic way of representing and interpolating labels, which allows straightforward extension of any kind of mixup to deep metric learning for a large class of loss functions.

PixLoc is a scene-agnostic neural network that estimates an accurate 6-DoF pose from an image and a 3D model. It is based on the direct alignment of multiscale deep features, casting camera localization as metric learning. PixLoc learns strong data priors by end-to-end training from pixels to pose and exhibits exceptional generalization to new scenes by separating model parameters and scene geometry. As the CNN never sees 3D points, PixLoc can generalize to any 3D structure available. This includes sparse SfM point clouds, dense depth maps from stereo or RGBD sensors, meshes, Lidar scans, but also lines and other primitives.

A trainable layer that encodes feature vectors onto 2 rotational coordinates $R_y(\theta),R_z(\gamma)$ for a unit sphere. Used to map data onto the Bloch Sphere surface for qubits