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Gabriel Tseng

gabriel.tseng@mila.quebec
PhD - McGill University
Supervisor
David Rolnick
Website
Github

Publications

Lightweight, Pre-trained Transformers for Remote Sensing Timeseries
Gabriel Tseng
Ruben Cartuyvels
Ivan Zvonkov
Mirali Purohit
David Rolnick
Hannah Kerner
Machine learning methods for satellite data have a range of societally relevant applications, but labels used to train models can be difficu… (see more)lt or impossible to acquire. Self-supervision is a natural solution in settings with limited labeled data, but current self-supervised models for satellite data fail to take advantage of the characteristics of that data, including the temporal dimension (which is critical for many applications, such as monitoring crop growth) and availability of data from many complementary sensors (which can significantly improve a model's predictive performance). We present Presto (the Pretrained Remote Sensing Transformer), a model pre-trained on remote sensing pixel-timeseries data. By designing Presto specifically for remote sensing data, we can create a significantly smaller but performant model. Presto excels at a wide variety of globally distributed remote sensing tasks and performs competitively with much larger models while requiring far less compute. Presto can be used for transfer learning or as a feature extractor for simple models, enabling efficient deployment at scale.
2023-04-27
ArXiv (preprint)
doi.org
openreview.net
Semi-Supervised Object Detection for Agriculture
Gabriel Tseng
Krisztina Sinkovics
Tom Watsham
David Rolnick
Thomas C. Walters
2023-01-26
AAAI.org/2023/Workshop/AIAFS (poster)
openreview.net
openreview.net
TIML: Task-Informed Meta-Learning for Agriculture
Gabriel Tseng
Hannah Kerner
David Rolnick
Labeled datasets for agriculture are extremely spatially imbalanced. When developing algorithms for data-sparse regions, a natural approach … (see more)is to use transfer learning from data-rich regions. While standard transfer learning approaches typically leverage only direct inputs and outputs, geospatial imagery and agricultural data are rich in metadata that can inform transfer learning algorithms, such as the spatial coordinates of data-points or the class of task being learned. We build on previous work exploring the use of meta-learning for agricultural contexts in data-sparse regions and introduce task-informed meta-learning (TIML), an augmentation to model-agnostic meta-learning which takes advantage of task-specific metadata. We apply TIML to crop type classification and yield estimation, and find that TIML significantly improves performance compared to a range of benchmarks in both contexts, across a diversity of model architectures. While we focus on tasks from agriculture, TIML could offer benefits to any meta-learning setup with task-specific metadata, such as classification of geo-tagged images and species distribution modelling.
2022-02-04
ArXiv (preprint)
arxiv.org
arxiv.org