Unsupervised Few-Shot Image Classification
15 papers with code • 4 benchmarks • 2 datasets
In contrast to (supervised) few-shot image classification, only the unlabeled dataset is available in the pre-training or meta-training stage for unsupervised few-shot image classification.
Benchmarks
These leaderboards are used to track progress in Unsupervised Few-Shot Image Classification
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mini-Imagenet
tieredImageNet
Most implemented papers
Self-Supervision Can Be a Good Few-Shot Learner
bbbdylan/unisiam
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Specifically, we maximize the mutual information (MI) of instances and their representations with a low-bias MI estimator to perform self-supervised pre-training.
Self-Supervised Learning For Few-Shot Image Classification
In this paper, we proposed to train a more generalized embedding network with self-supervised learning (SSL) which can provide robust representation for downstream tasks by learning from the data itself.
Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
Building on these insights and on advances in self-supervised learning, we propose a transfer learning approach which constructs a metric embedding that clusters unlabeled prototypical samples and their augmentations closely together.
Rethinking Class Relations: Absolute-relative Supervised and Unsupervised Few-shot Learning
ojss/samptransfer
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CVPR 2021
The majority of existing few-shot learning methods describe image relations with binary labels.
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
WonderSeven/ULDA
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Importantly, we highlight the value and importance of the distribution diversity in the augmentation-based pretext few-shot tasks, which can effectively alleviate the overfitting problem and make the few-shot model learn more robust feature representations.
Revisiting 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.
Meta-GMVAE: Mixture of Gaussian VAE for Unsupervised Meta-Learning
db-Lee/Meta-GMVAE
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ICLR 2021
Then, the learned model can be used for downstream few-shot classification tasks, where we obtain task-specific parameters by performing semi-supervised EM on the latent representations of the support and query set, and predict labels of the query set by computing aggregated posteriors.
Multi-level Second-order Few-shot Learning
The goal of multi-level feature design is to extract feature representations at different layer-wise levels of CNN, realizing several levels of visual abstraction to achieve robust few-shot learning.
Self-Supervised Class-Cognizant Few-Shot Classification
ojss/c3lr
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Unsupervised learning is argued to be the dark matter of human intelligence.
Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
In this work, we prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
