The helpfulness of category labels in semi-supervised learning depends on category structure

Vong, Wai Keen; Navarro, Daniel J.; Perfors, Amy

doi:10.3758/s13423-015-0857-9

Cited by 13 publications

(11 citation statements)

References 23 publications

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“…In doing so, researchers might also consider how different category learning challenges may alter the value of labels. For instance, while labels may generally be beneficial, their impact is likely to be reduced for more transparent categories (Vong, Navarro, & Perfors, ) or for extremely high‐dimensional spaces (Hinton & Salakhutdinov, ).…”

Section: Discussionmentioning

confidence: 99%

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A little labeling goes a long way: Semi‐supervised learning in infancy

LaTourrette

Waxman

2018

Developmental Science

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There is considerable evidence that labeling supports infants' object categorization. Yet in daily life, most of the category exemplars that infants encounter will remain unlabeled. Inspired by recent evidence from machine learning, we propose that infants successfully exploit this sparsely labeled input through "semi-supervised learning." Providing only a few labeled exemplars leads infants to initiate the process of categorization, after which they can integrate all subsequent exemplars, labeled or unlabeled, into their evolving category representations. Using a classic novelty preference task, we introduced 2-year-old infants (n = 96) to a novel object category, varying whether and when its exemplars were labeled. Infants were equally successful whether all exemplars were labeled (fully supervised condition) or only the first two exemplars were labeled (semi-supervised condition), but they failed when no exemplars were labeled (unsupervised condition). Furthermore, the timing of the labeling mattered: when the labeled exemplars were provided at the end, rather than the beginning, of familiarization (reversed semi-supervised condition), infants failed to learn the category. This provides the first evidence of semi-supervised learning in infancy, revealing that infants excel at learning from exactly the kind of input that they typically receive in acquiring real-world categories and their names.

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Section: Discussionmentioning

confidence: 99%

“…For instance, while labels may generally be beneficial, their impact is likely to be reduced for more transparent categories (Vong, Navarro, & Perfors, 2015) or for extremely high-dimensional spaces (Hinton & Salakhutdinov, 2006).…”

Section: G Ener Al Discussionmentioning

confidence: 99%

A little labeling goes a long way: Semi‐supervised learning in infancy

LaTourrette

Waxman

2018

Developmental Science

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“…Second, participants did use the no-feedback trials when the underlying categories were distinct and the gap between the categories was big. However, if the underlying categories were more ambiguous and the space between the categories was small but still existing, no effect of the nofeedback trials was found (Vong, Perfors, & Navarro, 2014). Third, Kalish, Zhu, and Rogers (2015) showed that the effect of the no-feedback trials depends on the age of the participants: young children (between 4 and 6 years old) were influenced by the no-feedback trials whereas no effects were found for older children (between 7 and 8 years old).…”

Section: The Impact Of No-feedback Trials In Semisupervised Learningmentioning

confidence: 98%

Semisupervised category learning facilitates the development of automaticity

Vandist

Storms

Bussche

2018

Atten Percept Psychophys

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In the human category of learning, learning is studied in a supervised, an unsupervised, or a semisupervised way. The rare human semisupervised category of learning studies all focus on early learning. However, the impact of the semisupervised category learning late in learning, when automaticity develops, is unknown. Therefore, in Experiment 1, all participants were first trained on the information-integration category structure for 2 days until they reached an expert level. Afterwards, half of the participants learned in a supervised way and the other half in a semisupervised way over two successive days. Both groups received an equal number of feedback trials. Finally, all participants took part in a test day where they were asked to respond as quickly as possible. Participants were significantly faster on this test in the semisupervised group than in the supervised group. This difference was not found on day 2, implying that the no-feedback trials in the semisupervised condition facilitated automaticity. Experiment 2 was designed to test whether the higher number of trials in the semisupervised condition of Experiment 1 caused the faster response times. We therefore created an almost supervised condition where participants almost always received feedback (95%) and an almost unsupervised condition where participants almost never received feedback (5%). All conditions now contained an equal number of trials to the semisupervised condition of Experiment 1. The results show that receiving feedback almost always or almost never led to slower response times than the semisupervised condition of Experiment 1. This confirms the advantage of semisupervised learning late in learning.

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“…Typically semi-supervised approaches work by making additional assumptions about the available data [89,92]. These include the smoothness assumption, i.e., samples close together in feature space are likely to be from the same class, the cluster assumption, i.e., samples in a cluster are likely to be from the same class, and the low density assumption, i.e., class boundaries are likely to be in low density areas of the feature space.…”

Section: Semi-supervised Deep Learning Strategies For Mrimentioning

confidence: 99%

Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

2020

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Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) of breast tissue are discussed. The algorithms are based on recent advances in multi-dimensional signal processing and aim to advance current state-of-the-art computer-aided detection and analysis of breast tumours when these are observed at various states of development. The topics discussed include image feature extraction, information fusion using radiomics, multi-parametric computer-aided classification and diagnosis using information fusion of tensorial datasets as well as Clifford algebra based classification approaches and convolutional neural network deep learning methodologies. The discussion also extends to semi-supervised deep learning and self-supervised strategies as well as generative adversarial networks and algorithms using generated confrontational learning approaches. In order to address the problem of weakly labelled tumour images, generative adversarial deep learning strategies are considered for the classification of different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence (AI) based framework for more robust image registration that can potentially advance the early identification of heterogeneous tumour types, even when the associated imaged organs are registered as separate entities embedded in more complex geometric spaces. Finally, the general structure of a high-dimensional medical imaging analysis platform that is based on multi-task detection and learning is proposed as a way forward. The proposed algorithm makes use of novel loss functions that form the building blocks for a generated confrontation learning methodology that can be used for tensorial DCE-MRI. Since some of the approaches discussed are also based on time-lapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The proposed framework can potentially reduce the costs associated with the interpretation of medical images by providing automated, faster and more consistent diagnosis.

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The helpfulness of category labels in semi-supervised learning depends on category structure

Cited by 13 publications

References 23 publications

A little labeling goes a long way: Semi‐supervised learning in infancy

A little labeling goes a long way: Semi‐supervised learning in infancy

Semisupervised category learning facilitates the development of automaticity

Pattern Classification Approaches for Breast Cancer Identification via MRI: State-Of-The-Art and Vision for the Future

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