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Found 9 papers, 5 papers with code
Image edge enhancement for effective image classification
A widely popular embraced method to improve the classification performance of neural networks is to incorporate data augmentations during the training process.
Adaptive Anchor Label Propagation for Transductive Few-Shot Learning
Our novel algorithm, Adaptive Anchor Label Propagation}, outperforms the standard label propagation algorithm by as much as 7% and 2% in the 1-shot and 5-shot settings respectively.
Adaptive manifold for imbalanced transductive few-shot learning
Our method exploits the underlying manifold of the labeled support examples and unlabeled queries by using manifold similarity to predict the class probability distribution per query.
Towards Automated Polyp Segmentation Using Weakly- and Semi-Supervised Learning and Deformable Transformers
Also, our framework can be utilized to fine-tune models trained on natural image segmentation datasets drastically improving their performance for polyp segmentation and impressively demonstrating superior performance to fully supervised fine-tuning.
Tensor feature hallucination for few-shot learning
We follow a different approach and investigate how a simple and straightforward synthetic data generation method can be used effectively.
Few-shot learning via tensor hallucination
Few-shot classification addresses the challenge of classifying examples given only limited labeled data.
A novel shape matching descriptor for real-time hand gesture recognition
In this work we present a novel shape matching methodology for real-time hand gesture recognition.
Iterative label cleaning for transductive and semi-supervised few-shot learning
Few-shot learning amounts to learning representations and acquiring knowledge such that novel tasks may be solved with both supervision and data being limited.
GENNI: Visualising the Geometry of Equivalences for Neural Network Identifiability
We propose an efficient algorithm to visualise symmetries in neural networks.
