Summary of Augmentations Vs Algorithms: What Works in Self-supervised Learning, by Warren Morningstar et al.
Augmentations vs Algorithms: What Works in Self-Supervised Learningby Warren Morningstar, Alex Bijamov, Chris Duvarney, Luke…
Augmentations vs Algorithms: What Works in Self-Supervised Learningby Warren Morningstar, Alex Bijamov, Chris Duvarney, Luke…
Unsupervised Graph Neural Architecture Search with Disentangled Self-supervisionby Zeyang Zhang, Xin Wang, Ziwei Zhang, Guangyao…
Synthetic Privileged Information Enhances Medical Image Representation Learningby Lucas Farndale, Chris Walsh, Robert Insall, Ke…
UniTable: Towards a Unified Framework for Table Recognition via Self-Supervised Pretrainingby ShengYun Peng, Aishwarya Chakravarthy,…
Reducing self-supervised learning complexity improves weakly-supervised classification performance in computational pathologyby Tim Lenz, Omar S.…
Decoupled Vertical Federated Learning for Practical Training on Vertically Partitioned Databy Avi Amalanshu, Yash Sirvi,…
Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shiftby Jun Chen, Weng-Keen Wong,…
On the Effectiveness of Distillation in Mitigating Backdoors in Pre-trained Encoderby Tingxu Han, Shenghan Huang,…
Bridging Diversity and Uncertainty in Active learning with Self-Supervised Pre-Trainingby Paul Doucet, Benjamin Estermann, Till…
Knowledge-guided EEG Representation Learningby Aditya Kommineni, Kleanthis Avramidis, Richard Leahy, Shrikanth NarayananFirst submitted to arxiv…