Uploaded March 2020 | Updated September 2026, 2 weeks ago
This video explains Meta Pseudo Labels! This is a really interesting algorithm for dynamically adapting the ground truth targets (y) while training a classification network. This is done by training a teacher network via gradient through a gradient to label images such that the classifier performs well on a held-out validation set. Thanks for watching! Please Subscribe!
Paper Links:
Meta Pseudo Labels: arxiv.org/pdf/2003.10580.pdf
UDA: arxiv.org/pdf/1904.12848.pdf
Generative Teaching Networks: arxiv.org/pdf/1912.07768.pdf
When Does Label Smoothing Help? arxiv.org/pdf/1906.02629.pdf
Self-Training with Noisy Student: arxiv.org/pdf/1911.04252.pdf
FixMatch: arxiv.org/pdf/2001.07685.pdf
RandAugment: arxiv.org/pdf/1909.13719.pdf
Thanks for watching, please subscribe!
This video explains Meta Pseudo Labels! This is a really interesting algorithm for dynamically adapting the ground truth targets (y) while training a classification network. This is done by training a teacher network via gradient through a gradient to label images such that the classifier performs well on a held-out validation set. Thanks for watching! Please Subscribe!
Paper Links:
Meta Pseudo Labels: arxiv.org/pdf/2003.10580.pdf
UDA: arxiv.org/pdf/1904.12848.pdf
Generative Teaching Networks: arxiv.org/pdf/1912.07768.pdf
When Does Label Smoothing Help? arxiv.org/pdf/1906.02629.pdf
Self-Training with Noisy Student: arxiv.org/pdf/1911.04252.pdf
FixMatch: arxiv.org/pdf/2001.07685.pdf
RandAugment: arxiv.org/pdf/1909.13719.pdf
Thanks for watching, please subscribe!
