Papers Unsupervised Few-Shot Image Classification
“Unsupervised Few-Shot Image Classification” 태그가 달린 논문 28편 · 필터 해제
BECLR: Batch Enhanced Contrastive Few-Shot Learning
Learning quickly from very few labeled samples is a fundamental attribute that separates machines and humans in the era of deep representation learning. Unsupervised few-shot learning (U-FSL) aspires to bridge this gap b…
Contrastive LearningFew-Shot Image ClassificationFew-Shot LearningRepresentation Learning+2Meta-DM: Applications of Diffusion Models on Few-Shot Learning
In the field of few-shot learning (FSL), extensive research has focused on improving network structures and training strategies. However, the role of data processing modules has not been fully explored. Therefore, in thi…
Few-Shot LearningUnsupervised Few-Shot Image ClassificationCMVAE: Causal Meta VAE for Unsupervised Meta-Learning
Unsupervised meta-learning aims to learn the meta knowledge from unlabeled data and rapidly adapt to novel tasks. However, existing approaches may be misled by the context-bias (e.g. background) from the training data. I…
Few-Shot Image Classificationimage-classificationImage ClassificationMeta-Learning+1Invariant and consistent: Unsupervised representation learning for few-shot visual recognition
Few-shot visual recognition aims to identify novel unseen classes with few labels while learning generalized prior knowledge from base classes. Recent ideas propose to explore this problem in an unsupervised setting, i.e…
Contrastive LearningRepresentation LearningUnsupervised Few-Shot Image ClassificationContrastive Prototypical Network with Wasserstein Confidence Penalty
Unsupervised few-shot learning aims to learn the inductive bias from unlabeled dataset for solving the novel few-shot tasks. The existing unsupervised few-shot learning models and the contrastive learning models follow a…
Contrastive LearningFew-Shot LearningInductive BiasUnsupervised Few-Shot Image Classification+1Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
Most few-shot image classification methods are trained based on tasks. Usually, tasks are built on base classes with a large number of labeled images, which consumes large effort. Unsupervised few-shot image classificati…
ClassificationClusteringFew-Shot Image Classificationimage-classification+2Self-Attention Message Passing for Contrastive Few-Shot Learning
Humans have a unique ability to learn new representations from just a handful of examples with little to no supervision. Deep learning models, however, require an abundance of data and supervision to perform at a satisfa…
Contrastive LearningFew-Shot LearningUnsupervised Few-Shot Image ClassificationUnsupervised Few-Shot LearningUnsupervised Few-shot Learning via Deep Laplacian Eigenmaps
Learning a new task from a handful of examples remains an open challenge in machine learning. Despite the recent progress in few-shot learning, most methods rely on supervised pretraining or meta-learning on labeled meta…
Few-Shot LearningLinear evaluationMeta-LearningSelf-Supervised Learning+2Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
The pioneering method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled da…
ClusteringDiversityMeta-LearningUnsupervised Few-Shot Image ClassificationSelf-Supervision Can Be a Good Few-Shot Learner
Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which prevents them from leveraging abundant unlabeled data. From an information-theoretic perspective, we propose an effective unsu…
cross-domain few-shot learningFew-Shot Image ClassificationFew-Shot LearningUnsupervised Few-Shot Image Classification+1Self-Supervised Class-Cognizant Few-Shot Classification
Unsupervised learning is argued to be the dark matter of human intelligence. To build in this direction, this paper focuses on unsupervised learning from an abundance of unlabeled data followed by few-shot fine-tuning on…
ClassificationContrastive LearningRe-RankingUnsupervised Few-Shot Image ClassificationMulti-level Second-order Few-shot Learning
We propose a Multi-level Second-order (MlSo) few-shot learning network for supervised or unsupervised few-shot image classification and few-shot action recognition. We leverage so-called power-normalized second-order bas…
Action RecognitionFew-Shot action recognitionFew Shot Action RecognitionFew-Shot Image Classification+4Unsupervised Meta-Learning via Latent Space Energy-based Model of Symbol Vector Coupling
Meta-learning aims to learn a model from a stream of tasks such that the model is able to generalize across tasks and rapidly adapt to new tasks. We propose to learn an energy-based model (EBM) in the latent space of a t…
Meta-LearningUnsupervised Few-Shot Image ClassificationTrainable Class Prototypes for Few-Shot Learning
Metric learning is a widely used method for few shot learning in which the quality of prototypes plays a key role in the algorithm. In this paper we propose the trainable prototypes for distance measure instead of the ar…
Few-Shot LearningMetric LearningSelf-Supervised LearningUnsupervised Few-Shot Image Classification+1Few-Shot Learning with Part Discovery and Augmentation from Unlabeled Images
Few-shot learning is a challenging task since only few instances are given for recognizing an unseen class. One way to alleviate this problem is to acquire a strong inductive bias via meta-learning on similar tasks. In t…
Few-Shot LearningInductive BiasMeta-LearningRepresentation Learning+1Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
Unsupervised learning aims to learn meaningful representations from unlabeled data which can captures its intrinsic structure, that can be transferred to downstream tasks. Meta-learning, whose objective is to learn to ge…
Meta-LearningUnsupervised Few-Shot Image ClassificationUnsupervised Few-Shot LearningVariational InferenceRevisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
Meta-learning has become a practical approach towards few-shot image classification, where "a strategy to learn a classifier" is meta-learned on labeled base classes and can be applied to tasks with novel classes. We rem…
Few-Shot Image ClassificationFew-Shot Learningimage-classificationImage Classification+3Shot in the Dark: Few-Shot Learning with No Base-Class Labels
Few-shot learning aims to build classifiers for new classes from a small number of labeled examples and is commonly facilitated by access to examples from a distinct set of 'base classes'. The difference in data distribu…
Few-Shot LearningInductive BiasSelf-Supervised LearningUnsupervised Few-Shot Image Classification+1Few-Shot Image Classification via Contrastive Self-Supervised Learning
Most previous few-shot learning algorithms are based on meta-training with fake few-shot tasks as training samples, where large labeled base classes are required. The trained model is also limited by the type of tasks. I…
ClassificationFew-Shot Image ClassificationFew-Shot LearningGeneral Classification+5Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
Most approaches in few-shot learning rely on costly annotated data related to the goal task domain during (pre-)training. Recently, unsupervised meta-learning methods have exchanged the annotation requirement for a reduc…
ClassificationFew-Shot LearningGeneral ClassificationMeta-Learning+4