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Found 6 papers, 3 papers with code
An Empirical Study on Clustering Pretrained Embeddings: Is Deep Strictly Better?
Recent research in clustering face embeddings has found that unsupervised, shallow, heuristic-based methods -- including $k$-means and hierarchical agglomerative clustering -- underperform supervised, deep, inductive methods.
Online Unsupervised Learning of Visual Representations and Categories
Real world learning scenarios involve a nonstationary distribution of classes with sequential dependencies among the samples, in contrast to the standard machine learning formulation of drawing samples independently from a fixed, typically uniform distribution.
von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning
Recent work has argued that classification losses utilizing softmax cross-entropy are superior not only for fixed-set classification tasks, but also by outperforming losses developed specifically for open-set tasks including few-shot learning and retrieval.
Geomorphological Analysis Using Unpiloted Aircraft Systems, Structure from Motion, and Deep Learning
We present a pipeline for geomorphological analysis that uses structure from motion (SfM) and deep learning on close-range aerial imagery to estimate spatial distributions of rock traits (size, roundness, and orientation) along a tectonic fault scarp.
Stochastic Prototype Embeddings
We propose a probabilistic method that treats embeddings as random variables.
Adapted Deep Embeddings: A Synthesis of Methods for $k$-Shot Inductive Transfer Learning
We hope our results will motivate a unification of research in weight transfer, deep metric learning, and few-shot learning.
