Semi-Supervised Discriminative Language Modeling with Out-of-Domain Text Data
Code (0)
등록된 구현이 없습니다.
Tasks
Language ModelingLanguage ModellingSpeech RecognitionSimilar Papers 제목 키워드 기반
Discriminative Consistent Domain Generation for Semi-supervised Learning
Deep learning based task systems normally rely on a large amount of manually labeled training data, which is expensive to obtain and subject to operator variations. Moreover, it does not always hold that the manually lab…
AnatomyDomain AdaptationImage GenerationSemi-Supervised Multi-Task Learning With Chest X-Ray Images
Discriminative models that require full supervision are inefficacious in the medical imaging domain when large labeled datasets are unavailable. By contrast, generative modeling---i.e., learning data generation and class…
Multi-Task LearningSegmentationIntrospective Generative Modeling: Decide Discriminatively
We study unsupervised learning by developing introspective generative modeling (IGM) that attains a generator using progressively learned deep convolutional neural networks. The generator is itself a discriminator, capab…
General ClassificationSafe Semi-Supervised Learning of Sum-Product Networks
In several domains obtaining class annotations is expensive while at the same time unlabelled data are abundant. While most semi-supervised approaches enforce restrictive assumptions on the data distribution, recent work…
Learning Consistent Deep Generative Models from Sparsely Labeled Data
We consider training deep generative models toward two simultaneous goals: discriminative classification and generative modeling using an explicit likelihood. While variational autoencoders (VAEs) offer a promising solut…
image-classificationImage Classification