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[HTML][HTML] Few-shot learning based on deep learning: A survey
W Zeng, Z **. As an important foundation, deep learning (DL) …
Self-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 …
prevents them from leveraging abundant unlabeled data. From an information-theoretic …
Few-shot learning with visual distribution calibration and cross-modal distribution alignment
Pre-trained vision-language models have inspired much research on few-shot learning.
However, with only a few training images, there exist two crucial problems:(1) the visual …
However, with only a few training images, there exist two crucial problems:(1) the visual …
A two-step data augmentation method based on generative adversarial network for hardness prediction of high entropy alloy
Z Yang, S Li, S Li, J Yang, D Liu - Computational Materials Science, 2023 - Elsevier
The machine learning (ML) has been widely applied in materials science research and has
made a lot of contributions. However, the performance of ML model is limited by the amount …
made a lot of contributions. However, the performance of ML model is limited by the amount …
Few-shot and meta-learning methods for image understanding: a survey
K He, N Pu, M Lao, MS Lew - International Journal of Multimedia …, 2023 - Springer
State-of-the-art deep learning systems (eg, ImageNet image classification) typically require
very large training sets to achieve high accuracies. Therefore, one of the grand challenges is …
very large training sets to achieve high accuracies. Therefore, one of the grand challenges is …
A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning
How to fast and accurately assess the severity level of COVID-19 is an essential problem,
when millions of people are suffering from the pandemic around the world. Currently, the …
when millions of people are suffering from the pandemic around the world. Currently, the …
Bridging the gap between few-shot and many-shot learning via distribution calibration
A major gap between few-shot and many-shot learning is the data distribution empirically
oserved by the model during training. In few-shot learning, the learned model can easily …
oserved by the model during training. In few-shot learning, the learned model can easily …
Self-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 …
domain during (pre-) training. Recently, unsupervised meta-learning methods have …
Contrastive prototype learning with augmented embeddings for few-shot learning
Most recent few-shot learning (FSL) methods are based on meta-learning with episodic
training. In each meta-training episode, a discriminative feature embedding and/or classifier …
training. In each meta-training episode, a discriminative feature embedding and/or classifier …
小样本图像分类研究综述.
安胜彪， 郭昱岐， 白宇， 王腾博 - Journal of Frontiers of …, 2023 - search.ebscohost.com
**年来, 借助大规模数据集和庞大的计算资源, 以深度学**为代表的人工智能算法在诸多领域
取得成功. 其中计算机视觉领域的图像分类技术蓬勃发展, 并涌现出许多成熟的视觉任务分类 …
取得成功. 其中计算机视觉领域的图像分类技术蓬勃发展, 并涌现出许多成熟的视觉任务分类 …