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Aarash Feizi

feiziaar@mila.quebec
Doctorat - McGill University
Superviseur⋅e principal⋅e
Reihaneh Rabbany
Co-superviseur⋅e
Adriana Romero Soriano
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Publications

GPS-SSL: Guided Positive Sampling to Inject Prior Into Self-Supervised Learning
Aarash Feizi
Randall Balestriero
Arantxa Casanova
Adriana Romero Soriano
Reihaneh Rabbany
We propose Guided Positive Sampling Self-Supervised Learning (GPS-SSL), a general method to inject a priori knowledge into Self-Supervised L… (voir plus)earning (SSL) positive samples selection. Current SSL methods leverage Data-Augmentations (DA) for generating positive samples and incorporate prior knowledge - an incorrect, or too weak DA will drastically reduce the quality of the learned representation. GPS-SSL proposes instead to design a metric space where Euclidean distances become a meaningful proxy for semantic relationship. In that space, it is now possible to generate positive samples from nearest neighbor sampling. Any prior knowledge can now be embedded into that metric space independently from the employed DA. From its simplicity, GPS-SSL is applicable to any SSL method, e.g. SimCLR or BYOL. A key benefit of GPS-SSL is in reducing the pressure in tailoring strong DAs. For example GPS-SSL reaches 85.58% on Cifar10 with weak DA while the baseline only reaches 37.51%. We therefore move a step forward towards the goal of making SSL less reliant on DA. We also show that even when using strong DAs, GPS-SSL outperforms the baselines on under-studied domains. We evaluate GPS-SSL along with multiple baseline SSL methods on numerous downstream datasets from different domains when the models use strong or minimal data augmentations. We hope that GPS-SSL will open new avenues in studying how to inject a priori knowledge into SSL in a principled manner.
2024-01-03
ArXiv (prépublication)
doi.org
openreview.net
Party Prediction for Twitter
Kellin Pelrine
Anne Imouza
Zachary Yang
Jacob-Junqi Tian
Sacha Lévy
Gabrielle Desrosiers-Brisebois
Aarash Feizi
C'ecile Amadoro
André Blais
Jean-François Godbout
Reihaneh Rabbany
2023-08-25
ArXiv (prépublication)
doi.org
arxiv.org
Revisiting Hotels-50K and Hotel-ID
Aarash Feizi
Arantxa Casanova
Adriana Romero Soriano
Reihaneh Rabbany
In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels-50K and Hotel-ID. The revisited versions prov… (voir plus)ide evaluation setups with different levels of difficulty to better align with the intended real-world application, i.e. countering human trafficking. Real-world scenarios involve hotels and locations that are not captured in the current data sets, therefore it is important to consider evaluation settings where classes are truly unseen. We test this setup using multiple state-of-the-art image retrieval models and show that as expected, the models’ performances decrease as the evaluation gets closer to the real-world unseen settings. The rankings of the best performing models also change across the different evaluation settings, which further motivates using the proposed revisited datasets.
2022-07-20
ArXiv (prépublication)
doi.org
arxiv.org