Top Tweets for #NoisyStudent
Success of self-training extends to object detection and semantic segmentation! Key to SOTA results in PASCAL semantic segmentation is the usage of #NoisyStudent checkpoints EfficientNet-L2 :) https://t.co/0HJkq1AoxA
We researchers love pre-training. Our new paper shows that pre-training is unhelpful when we have a lot of labeled data. In contrast, self-training works well even when we have a lot of labeled data. SOTA on PASCAL segmentation & COCO detection.
Link: https://t.co/Cw0HabUY9F
#NoisyStudent Training (NST) now extends to speech recognition with SOTA results on LibriSpeech. Iterative self-training continues to provide strong gains in noisy environments (up to 2 generations). Key ideas: adaptive SpecAugment & gradational filtering. https://t.co/SRx1abQa3i
Some new progress on Semi-supervised learning for speech recognition. Noisy student training improves accuracy on the LibriSpeech benchmark. The gains are especially big on the noisy test set (“Test-other”).
Link: https://t.co/4yE6RuGKV5
Nice recent tutorial on semi-supervised learning, covering our recent works on UDA (https://t.co/Uoj2Rb7Etx) and #NoisyStudent (https://t.co/cBvSLwq9O3). It also highlights VAT, Pi-Model, MeanTeacher, and MixMatch. Slides: https://t.co/aUmG45RmW6
Been looking to learn more about Semi-Supervised Learning or Unsupervised Distribution Alignment?
One hour tutorials, respectively by @Aravind7694 and @peterxichen, for CS294-158:
https://t.co/Jyq9sNwwp9
It is also important to note that adding noise to the student, training equal-or-larger students, and iterative self-training is a novel combination that defines the success of #NoisyStudent on ImageNet (https://t.co/cBvSLwq9O3, to appear in #CVPR).
What's the difference between recent works in semi-supervised learning? Key: consistency training (UDA, FixMatch, ReMixMatch, ICT, etc.) and self-training (#NoisyStudent), see photo below and my recent talk (https://t.co/DBTQH2xHHL with bonus slides on #MeenaBot!).
Graph-Bert, Noisy Student, and Meena the open-domain chatbot. Is it a #MachineLearning recap, or an episode of Sesame Street... Find out in the February edition of Baseline, our ML newsletter: https://t.co/gvo8jNddGl #chatbot #meena #ai #NoisyStudent #ThincAI
We have started releasing #NoisyStudent code, first on SVHN for the community to quickly try. For ImageNet, we are looking into other public datasets as unlabeled data & will share soon, so stay tune!
https://t.co/0HJkq1AoxA
@quocleix @QizheXie
Last week we released the checkpoints for SOTA ImageNet models trained by NoisyStudent. Due to popular demand, we’ve also opensourced an implementation of NoisyStudent. The code uses SVHN for demonstration purposes.
Link: https://t.co/t3YK6Aiu5Q
Paper: https://t.co/ZYDaef6sdp
Amazing progress of semisupervised learning! It turns out that the best learning comes from #MeanTeacher and #NoisyStudent !
Our #NoisyStudent checkpoints are now in @PyTorch (courtesy of @wightmanr, https://t.co/05KKaE3W0z). Here's another look at the field's 10-year progress on ImageNet (from @paperswithcode)!
Our #NoisyStudent checkpoints are now in @PyTorch (courtesy of @wightmanr, https://t.co/05KKaE3W0z). Here's another look at the field's 10-year progress on ImageNet (from @paperswithcode)!
@karpathy 300M unlabelled images vs 3.5B labelled images. The FSD industry has been dying for something like this. The weekend warrior engineers thank you, Google. @hardmaru @JeffDean #NoisyStudent #EfficientNet
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