This paper tackles the problem of semi-supervised learning when set labeled samples is limited to a small number images per class, typically less than 10, that we refer as barely-supervised learning. We analyze in depth behavior state-of-the-art method, FixMatch, which relies on weakly-augmented version an image obtain supervision signal for more strongly-augmented version. show it frequently f...

This thesis argues that successful semi-supervised learning is improved by learning many functions at once in a coupled manner. Given knowledge about constraints between functions to be learned (e.g., f1(x) → ¬f2(x)), forcing the models that are learned to obey these constraints can yield a more constrained, and therefore easier, set of learning problems. We apply these ideas to bootstrap learn...

Semi-supervised learning uses both labeled and unlabeled data to perform an otherwise supervised learning or unsupervised learning task. In the former case, there is a distinction between inductive semi-supervised learning and transductive learning. In inductive semi-supervised learning, the learner has both labeled training data {(xi, yi)}i=1 iid ∼ p(x, y) and unlabeled training data {xi} i=l+...

For many classification problems, unlabeled training data are inexpensive and readily available, whereas labeling training data imposes costs. Semi-supervised classification algorithms aim at utilizing information contained in unlabeled data in addition to the (few) labeled data. Semi-supervised (for an example, see Seeger, 2001) has a long tradition in statistics (Cooper & Freeman, 1970); much...

Most learning algorithms for factor graphs require complete inference over the dataset or an instance before making an update to the parameters. SampleRank is a rank-based learning framework that alleviates this problem by updating the parameters during inference. Most semi-supervised learning algorithms also rely on the complete inference, i.e. calculating expectations or MAP configurations. W...

In this paper we propose a framework for supervised and semi-supervised learning based on reformulating the learning problem as a regularized Fredholm integral equation. Our approach fits naturally into the kernel framework and can be interpreted as constructing new data-dependent kernels, which we call Fredholm kernels. We proceed to discuss the “noise assumption” for semi-supervised learning ...

Discriminative learning framework is one of the very successful fields of machine learning. The methods of this paradigm, such as Boosting, and Support Vector Machines have significantly advanced the state-of-the-art for classification by improving the accuracy and by increasing the applicability of machine learning methods. Recently there has been growing interest to generalize discrimative le...

We explore a novel approach to semi-supervised learning. This approach is contrary to the common approach in that the unlabeled examples serve to "muffle," rather than enhance, the guidance provided by the labeled examples. We provide several variants of the basic algorithm and show experimentally that they can achieve significantly higher AUC than boosted trees, random forests and logistic reg...

We present two approaches that use unlabeled data to improve sequence learning with recurrent networks. The first approach is to predict what comes next in a sequence, which is a conventional language model in natural language processing. The second approach is to use a sequence autoencoder, which reads the input sequence into a vector and predicts the input sequence again. These two algorithms...

In this paper, we describe the stochastic semi-supervised learning approach that we used in our submission to all six tasks in 2009-2010 Active Learning Challenge. The method is designed to tackle the binary classification problem under the condition that the number of labeled data points is extremely small and the two classes are highly imbalanced. It starts with only one positive seed given b...