Papers Semi-Supervised Image Classification
“Semi-Supervised Image Classification” 태그가 달린 논문 169편 · 필터 해제
Semi-supervised Sequence-to-sequence ASR using Unpaired Speech and Text
Sequence-to-sequence automatic speech recognition (ASR) models require large quantities of data to attain high performance. For this reason, there has been a recent surge in interest for unsupervised and semi-supervised …
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Semi-Supervised Image Classificationspeech-recognition+3Unsupervised Data Augmentation for Consistency Training
Semi-supervised learning lately has shown much promise in improving deep learning models when labeled data is scarce. Common among recent approaches is the use of consistency training on a large amount of unlabeled data …
Data AugmentationImage AugmentationImage ClassificationSemi-Supervised Image Classification+3Deep Sparse Representation-based Classification
We present a transductive deep learning-based formulation for the sparse representation-based classification (SRC) method. The proposed network consists of a convolutional autoencoder along with a fully-connected layer. …
ClassificationDecoderGeneral ClassificationImage Classification+2Interpolation Consistency Training for Semi-Supervised Learning
We introduce Interpolation Consistency Training (ICT), a simple and computation efficient algorithm for training Deep Neural Networks in the semi-supervised learning paradigm. ICT encourages the prediction at an interpol…
General ClassificationSemi-Supervised Image ClassificationSemi-Supervised Deep Learning with Memory
We consider the semi-supervised multi-class classification problem of learning from sparse labelled and abundant unlabelled training data. To address this problem, existing semi-supervised deep learning methods often rel…
Deep LearningGeneral Classificationimage-classificationImage Classification+2Representation Learning with Contrastive Predictive Coding
While supervised learning has enabled great progress in many applications, unsupervised learning has not seen such widespread adoption, and remains an important and challenging endeavor for artificial intelligence. In th…
Reinforcement LearningRepresentation LearningSelf-Supervised Image ClassificationSemi-Supervised Image ClassificationTwo Methods for Domain Adaptation of Bilingual Tasks: Delightfully Simple and Broadly Applicable
Bilingual tasks, such as bilingual lexicon induction and cross-lingual classification, are crucial for overcoming data sparsity in the target language. Resources required for such tasks are often out-of-domain, thus doma…
Bilingual Lexicon InductionClassificationDomain AdaptationGeneral Classification+6There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average
Presently the most successful approaches to semi-supervised learning are based on consistency regularization, whereby a model is trained to be robust to small perturbations of its inputs and parameters. To understand con…
Domain AdaptationSemi-Supervised Image ClassificationUnsupervised Feature Learning via Non-Parametric Instance Discrimination
Neural net classifiers trained on data with annotated class labels can also capture apparent visual similarity among categories without being directed to do so. We study whether this observation can be extended beyond th…
General Classificationobject-detectionObject DetectionRetrieval+2Unsupervised Learning using Pretrained CNN and Associative Memory Bank
Deep Convolutional features extracted from a comprehensive labeled dataset, contain substantial representations which could be effectively used in a new domain. Despite the fact that generic features achieved good result…
Few-Shot Image ClassificationFine-Grained Image ClassificationImage ClassificationObject Recognition+1CapsuleGAN: Generative Adversarial Capsule Network
We present Generative Adversarial Capsule Network (CapsuleGAN), a framework that uses capsule networks (CapsNets) instead of the standard convolutional neural networks (CNNs) as discriminators within the generative adver…
General ClassificationGenerative Adversarial Networkimage-classificationImage Classification+1Structured Generative Adversarial Networks
We study the problem of conditional generative modeling based on designated semantics or structures. Existing models that build conditional generators either require massive labeled instances as supervision or are unable…
image-classificationImage ClassificationSemi-Supervised Image ClassificationStyle Transfermixup: Beyond Empirical Risk Minimization
Large deep neural networks are powerful, but exhibit undesirable behaviors such as memorization and sensitivity to adversarial examples. In this work, we propose mixup, a simple learning principle to alleviate these issu…
Domain GeneralizationImage ClassificationMemorizationOut-of-Distribution Generalization+1Triangle Generative Adversarial Networks
A Triangle Generative Adversarial Network ($\Delta$-GAN) is developed for semi-supervised cross-domain joint distribution matching, where the training data consists of samples from each domain, and supervision of domain …
AttributeGenerative Adversarial Networkimage-classificationImage Classification+4Improved Regularization of Convolutional Neural Networks with Cutout
Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks. However, due to the model capacity required to capture such representations…
Data AugmentationDomain GeneralizationImage AugmentationImage Classification+2Good Semi-supervised Learning that Requires a Bad GAN
Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why g…
General ClassificationSemi-Supervised Image ClassificationVirtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
We propose a new regularization method based on virtual adversarial loss: a new measure of local smoothness of the conditional label distribution given input. Virtual adversarial loss is defined as the robustness of the …
Semi-Supervised Image ClassificationMean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
The recently proposed Temporal Ensembling has achieved state-of-the-art results in several semi-supervised learning benchmarks. It maintains an exponential moving average of label predictions on each training example, an…
Semi-Supervised Image ClassificationSemi-Supervised RGBD Semantic SegmentationSemi-Supervised Semantic SegmentationSource Free Object DetectionSemi-Supervised Learning with Context-Conditional Generative Adversarial Networks
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based …
Image ClassificationSemi-Supervised Image ClassificationTemporal Ensembling for Semi-Supervised Learning
In this paper, we present a simple and efficient method for training deep neural networks in a semi-supervised setting where only a small portion of training data is labeled. We introduce self-ensembling, where we form a…
General ClassificationSemi-Supervised Image Classification