no code implementations • 9 Nov 2022 • Tyler R. Scott, Ting Liu, Michael C. Mozer, Andrew C. Gallagher
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.
1 code implementation • 13 Sep 2021 • Mengye Ren, Tyler R. Scott, Michael L. Iuzzolino, Michael C. Mozer, Richard Zemel
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.
1 code implementation • ICCV 2021 • Tyler R. Scott, Andrew C. Gallagher, Michael C. Mozer
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.
no code implementations • 27 Sep 2019 • Zhiang Chen, Tyler R. Scott, Sarah Bearman, Harish Anand, Devin Keating, Chelsea Scott, J Ramon Arrowsmith, Jnaneshwar Das
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.
no code implementations • 25 Sep 2019 • Tyler R. Scott, Karl Ridgeway, Michael C. Mozer
We propose a probabilistic method that treats embeddings as random variables.
2 code implementations • 22 May 2018 • Tyler R. Scott, Karl Ridgeway, Michael C. Mozer
We hope our results will motivate a unification of research in weight transfer, deep metric learning, and few-shot learning.