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A survey on stability of learning with limited labelled data and its sensitivity to the effects of randomness
Learning with limited labelled data, such as prompting, in-context learning, fine-tuning, meta-
learning, or few-shot learning, aims to effectively train a model using only a small amount of …
learning, or few-shot learning, aims to effectively train a model using only a small amount of …
Instance-based max-margin for practical few-shot recognition
In order to mimic the human few-shot learning (FSL) ability better and to make FSL closer to
real-world applications this paper proposes a practical FSL (pFSL) setting. pFSL is based on …
real-world applications this paper proposes a practical FSL (pFSL) setting. pFSL is based on …
NegCosIC: Negative cosine similarity-invariance-covariance regularization for few-shot learning
Few-shot learning continues to pose a challenge as it is inherently difficult for visual
recognition models to generalize with limited labeled examples. When the training data is …
recognition models to generalize with limited labeled examples. When the training data is …
Task Attribute Distance for Few-Shot Learning: Theoretical Analysis and Applications
Few-shot learning (FSL) aims to learn novel tasks with very few labeled samples by
leveraging experience from\emph {related} training tasks. In this paper, we try to understand …
leveraging experience from\emph {related} training tasks. In this paper, we try to understand …
Towards Stabilized Few-Shot Object Detection with Less Forgetting via Sample Normalization
Y Ren, M Yang, Y Han, W Li - Sensors, 2024 - mdpi.com
Few-shot object detection is a challenging task aimed at recognizing novel classes and
localizing with limited labeled data. Although substantial achievements have been obtained …
localizing with limited labeled data. Although substantial achievements have been obtained …
[HTML][HTML] Few-Shot Learning in Wi-Fi-Based Indoor Positioning
This paper explores the use of few-shot learning in Wi-Fi-based indoor positioning, utilizing
convolutional neural networks (CNNs) combined with meta-learning techniques to enhance …
convolutional neural networks (CNNs) combined with meta-learning techniques to enhance …
Building one-shot semi-supervised (BOSS) learning up to fully supervised performance
Reaching the performance of fully supervised learning with unlabeled data and only
labeling one sample per class might be ideal for deep learning applications. We …
labeling one sample per class might be ideal for deep learning applications. We …
Few-Shot Object Detection Via Stabilized Meta-Learning Framework with Sample Normalization
Y Ren, H Jiang, M Yang - Available at SSRN 4576880 - papers.ssrn.com
Few-shot object detection has gained widespread attention due to its capability to transfer
knowledge from base classes to novel classes. However, existing methods suffer the …
knowledge from base classes to novel classes. However, existing methods suffer the …