Papers Semi-Supervised Image Classification
“Semi-Supervised Image Classification” 태그가 달린 논문 169편 · 필터 해제
Semi-Supervised Learning with Normalizing Flows
Normalizing flows transform a latent distribution through an invertible neural network for a flexible and pleasingly simple approach to generative modelling, while preserving an exact likelihood. We propose FlowGMM, an e…
image-classificationImage ClassificationSemi-Supervised Image ClassificationSemi-Supervised Text ClassificationSESS: Self-Ensembling Semi-Supervised 3D Object Detection
The performance of existing point cloud-based 3D object detection methods heavily relies on large-scale high-quality 3D annotations. However, such annotations are often tedious and expensive to collect. Semi-supervised l…
3D Object Detectionimage-classificationImage ClassificationObject+3Triple Generative Adversarial Networks
We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax game consisting of a generator, a classifie…
ClassificationConditional Image GenerationData AugmentationGeneral Classification+3RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms
Semi-Supervised Learning (SSL) algorithms have shown great potential in training regimes when access to labeled data is scarce but access to unlabeled data is plentiful. However, our experiments illustrate several shortc…
Deep LearningSemi-Supervised Image ClassificationSelf-Supervised Learning of Pretext-Invariant Representations
The goal of self-supervised learning from images is to construct image representations that are semantically meaningful via pretext tasks that do not require semantic annotations for a large training set of images. Many …
Contrastive Learningobject-detectionObject DetectionRepresentation Learning+3Flow Contrastive Estimation of Energy-Based Models
This paper studies a training method to jointly estimate an energy-based model and a flow-based model, in which the two models are iteratively updated based on a shared adversarial value function. This joint training met…
Image GenerationSemi-Supervised Image ClassificationEnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations
Deep neural networks have been successfully applied to many real-world applications. However, such successes rely heavily on large amounts of labeled data that is expensive to obtain. Recently, many methods for semi-supe…
Image ClassificationSemi-Supervised Image ClassificationReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
We improve the recently-proposed "MixMatch" semi-supervised learning algorithm by introducing two new techniques: distribution alignment and augmentation anchoring. Distribution alignment encourages the marginal distribu…
Image ClassificationSemi-Supervised Image ClassificationAdversarial Transformations for Semi-Supervised Learning
We propose a Regularization framework based on Adversarial Transformations (RAT) for semi-supervised learning. RAT is designed to enhance robustness of the output distribution of class prediction for a given data against…
General Classificationimage-classificationImage ClassificationSemi-Supervised Image ClassificationDual Student: Breaking the Limits of the Teacher in Semi-supervised Learning
Recently, consistency-based methods have achieved state-of-the-art results in semi-supervised learning (SSL). These methods always involve two roles, an explicit or implicit teacher model and a student model, and penaliz…
Semi-Supervised Image ClassificationUnsupervised Domain AdaptationRepetitive Reprediction Deep Decipher for Semi-Supervised Learning
Most recent semi-supervised deep learning (deep SSL) methods used a similar paradigm: use network predictions to update pseudo-labels and use pseudo-labels to update network parameters iteratively. However, they lack the…
Semi-Supervised Image ClassificationPseudo-Labeling Curriculum for Unsupervised Domain Adaptation
To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation. However, the existence of false pseudo-labels, which may have a detrimental influ…
ClusteringDomain AdaptationSemi-Supervised Image ClassificationUnsupervised Domain AdaptationLarge Scale Adversarial Representation Learning
Adversarially trained generative models (GANs) have recently achieved compelling image synthesis results. But despite early successes in using GANs for unsupervised representation learning, they have since been supersede…
Contrastive LearningImage GenerationRepresentation LearningSelf-Supervised Image Classification+2Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning
Recent advances in semi-supervised learning have shown tremendous potential in overcoming a major barrier to the success of modern machine learning algorithms: access to vast amounts of human-labeled training data. Previ…
image-classificationImage ClassificationMulti-Task LearningSemi-Supervised Image ClassificationEnergy Models for Better Pseudo-Labels: Improving Semi-Supervised Classification with the 1-Laplacian Graph Energy
Semi-supervised classification is a great focus of interest, as in real-world scenarios obtaining labels is expensive, time-consuming and might require expert knowledge. This has motivated the fast development of semi-su…
BenchmarkingMulti-class ClassificationSemi-Supervised Image ClassificationManifold Graph with Learned Prototypes for Semi-Supervised Image Classification
Recent advances in semi-supervised learning methods rely on estimating the categories of unlabeled data using a model trained on the labeled data (pseudo-labeling) and using the unlabeled data for various consistency-bas…
ClassificationGeneral Classificationimage-classificationImage Classification+1Data-Efficient Image Recognition with Contrastive Predictive Coding
Human observers can learn to recognize new categories of images from a handful of examples, yet doing so with artificial ones remains an open challenge. We hypothesize that data-efficient recognition is enabled by repres…
Contrastive LearningGeneral Classificationobject-detectionObject Detection+3Semi-Supervised Learning by Augmented Distribution Alignment
In this work, we propose a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-supervised learning due to the limited n…
Domain AdaptationSemi-Supervised Image ClassificationS4L: Self-Supervised Semi-Supervised Learning
This work tackles the problem of semi-supervised learning of image classifiers. Our main insight is that the field of semi-supervised learning can benefit from the quickly advancing field of self-supervised visual repres…
General Classificationimage-classificationImage ClassificationRepresentation Learning+1MixMatch: A Holistic Approach to Semi-Supervised Learning
Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised l…
Image ClassificationSemi-Supervised Image Classification