May 17, 2019 · MixMatch Mixmatch, which is the novel approach presented in the paper, smartly combines 3 paradigms of SSL which were previously used separately. Consistency regularization — This is introduced by...
Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that guesses low-entropy labels for data-augmented un-
Semi-supervised learning [6] (SSL) seeks to largely alleviate the need for labeled data by allowing a model to leverage unlabeled data. Many recent approaches ...
Through extensive experiments on semi-supervised and privacy-preserving learning, the authors found that MixMatch exhibited significantly improved performance ...
In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for data-augmented unlabeled...
May 06, 2019 · MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets.
In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for …
when the mismatch rate is 60% (λ M = 0.1,0.25,0.5). ... Papernot, N., Oliver, A., Raffel, C.A.: Mixmatch: a holistic approach to semi-supervised learning.
In this section, we introduce MixMatch, our proposed semi-supervised learning method. MixMatch is a “holistic” approach which incorporates ideas and components from the dominant paradigms for SSL discussed in section 2. Given a batch X of labeled examples with one-hot targets (representing
06.05.2019 · MixMatch: A Holistic Approach to Semi-Supervised Learning David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, Colin Raffel Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets.
MixMatch: A Holistic Approach to Semi-Supervised Learning NeurIPS 2019 · David Berthelot , Nicholas Carlini , Ian Goodfellow , Nicolas Papernot , Avital Oliver , Colin Raffel · Edit social preview Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets.