19 citations · 42 across the 13 of their papers we have counts for
8 papers · 1 filter
Masked Swingers: Harnessing Data Augmentation to Advance Autoencoders for Self-Supervised Learning
Anthony Fuller, Scott C. Lowe, Daniel G. Kyrollos +3
Self-supervised learning (SSL) removes the need for annotations and makes models that are capable across more domains than supervised learning. The autoencoder SSL framework learns…
Planetary Feature Fields are Scalable Earth Representations
Arjun Rao, Sebastian Loeschcke, Anthony Fuller +3
Satellite observations, precomputed embeddings, and map products describe the same evolving Earth, yet are stored as independent, petabyte-scale data products. Their continued grow…
LookThere! Sparse Vision by Reinforced Selection
Sreehari Rammohan, Yousef Yassin, Anthony Fuller +3
Vision transformers typically treat every image token as equally important, yet for most tasks in computer vision only a fraction are needed. Adaptive computation methods accelerat…
Changing Modalities: Adapting Remote Sensing Models to New Satellites and Sensors
Tim G. Zhou, Anthony Fuller, Geoff Pleiss +1
Machine learning models for remote sensing are trained and deployed on a static set of modalities. However, as we equip newer satellites with novel sensors and retire old ones, pra…
No One Knows the State of the Art in Geospatial Foundation Models
Isaac Corley, Nils Lehmann, Caleb Robinson +6
Geospatial foundation models (GFMs) have been proposed as generalizable backbones for disaster response, land-cover mapping, food-security monitoring, and other high-stakes Earth-o…
LookWhen? Fast Video Recognition by Learning When, Where, and What to Compute
Ali Salamatian, Anthony Fuller, Pritam Sarkar +3
Transformers dominate video recognition. They split videos into tokens, and processing them has expensive superlinear computational cost. Yet videos are filled with redundancy, so…
Self-Distillation of Hidden Layers for Self-Supervised Representation Learning
Scott C. Lowe, Anthony Fuller, Sageev Oore +2
The landscape of self-supervised learning (SSL) is currently dominated by generative approaches (e.g. MAE) that reconstruct raw low-level data, and predictive approaches (e.g. I-JE…
Self-Soupervision: Cooking Model Soups without Labels
Anthony Fuller, James R. Green, Evan Shelhamer
Model soups are strange and strangely effective combinations of parameters. They take a model (the stock), fine-tune it into multiple models (the ingredients), and then mix their p…