Imaging without labels
نویسندگان
چکیده
منابع مشابه
Classifying Documents Without Labels
Automatic classification of documents is an important area of research with many applications in the fields of document searching, forensics and others. Methods to perform classification of text rely on the existence of a sample of documents whose class labels are known. However, in many situations, obtaining this sample may not be an easy (or even possible) task. Consider for instance, a set o...
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This paper considers the challenge of evaluating a set of classifiers, as done in shared task evaluations like the KDD Cup or NIST TREC, without expert labels. While expert labels provide the traditional cornerstone for evaluating statistical learners, limited or expensive access to experts represents a practical bottleneck. Instead, we seek methodology for estimating performance of the classif...
متن کاملMining on Manifolds: Metric Learning without Labels
In this work we present a novel unsupervised framework for hard training example mining. The only input to the method is a collection of images relevant to the target application and a meaningful initial representation, provided e.g. by pre-trained CNN. Positive examples are distant points on a single manifold, while negative examples are nearby points on different manifolds. Both types of exam...
متن کاملLearning without Labels and Nonnegative Tensor Factorization
Special thanks to my parents and friends for their love and support. 1 3-way PARAFAC model: The tensor is represented as a linear combination of r rank-1 tensors. This will provide a rank-r approximation to the original A plot of the loglikelihood functions (θ) in the case of classification for k = 1 (left, θ true = 0.75) and k = 2 (right, θ true = (0.8, 0.6) 3 A plot of the loglikelihood funct...
متن کاملUnsupervised Supervised Learning II: Margin-Based Classification without Labels
Many popular linear classifiers, such as logistic regression, boosting, or SVM, are trained by optimizing margin-based risk functions. Traditionally, these risk functions are computed based on a labeled dataset. We develop a novel technique for estimating such risks using only unlabeled data and knowledge of p(y). We prove that the proposed risk estimator is consistent on high-dimensional datas...
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ژورنال
عنوان ژورنال: Nature Methods
سال: 2014
ISSN: 1548-7091,1548-7105
DOI: 10.1038/nmeth.3129