Semi-Supervised Learning

By Olivier Chapelle, Bernhard Schlkopf, Alexander Zien. 2006

1,214 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Representation & Self-Supervised Learning

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Representation & Self-Supervised Learning

Citations in the corpus

S4L: Self-Supervised Semi-Supervised Learning (2019, background): “Given the wide variety of semi-supervised learning techniques proposed in the literature, we refer to [4] for an extensive survey.”
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence (2020, neutral): “Broader introductions are provided in [60, 61, 6].”
Biological structure and function emerge from scaling unsupervised learning to 250 million protein sequences (2019, methodology): “For the analysis in Section 4.3, we deﬁne various translation operations on the representation space of the diverse orthology dataset described above.”
Evaluating Protein Transfer Learning with TAPE (2019, background): “The fields of computer vision and natural language processing have been dealing with the question of how to learn from unlabeled data for years [25].”
A tutorial on spectral clustering (2007, background): “(2002), Meila and Shi (2001), and Ding (2004). Subsequently, spectral clustering has been extended to many non-standard settings, for example spectral clustering applied to the co-clustering problem (Dhillon, 2001), spectral clustering with additional side information (Joachims, 2003) connections be”
Interpolation Consistency Training for Semi-Supervised Learning (2019, methodology): “These methods are motivated by the low-density separation assumption (Chapelle et al., 2010), and as such push the decision boundary to lie in the low-density regions of the input space, achieving larger classification margins.”
Are Labels Required for Improving Adversarial Robustness? (2019, background): “The semisupervised learning approach [11] which, in addition to labeled data, also uses unlabeled data to learn better models is particularly relevant to our work.”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “in Adaptation for Visual Applications: A Comprehensive Survey 33 6.2 DA related to traditional ML methods Semi-supervised learning. DA can be seen as a particular case of the semi-supervised learning [248, 249], where, similarly to the majority of DA approaches, unlabeled data is exploited to remedy”
From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification (2016, background): “This scenario is also relevant for “learning with a probabilistic teacher” (Agrawala, 1970) and semi-supervised learning (Chapelle et al., 2006), as it can model label uncertainty.”
Detection of traffic signs in real-world images: The German traffic sign detection benchmark (2013, background): “It could be used for semi-supervised [28] and transductive learning [29], but this is explicitly not in the scope of our benchmark.”