19 citations · 19 across the 5 of their papers we have counts for
5 papers
Towards A Conceptually Simple Defensive Approach for Few-shot classifiers Against Adversarial Support Samples
Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun +3
Few-shot classifiers have been shown to exhibit promising results in use cases where user-provided labels are scarce. These models are able to learn to predict novel classes simply…
Detection of Adversarial Supports in Few-shot Classifiers Using Self-Similarity and Filtering
Yi Xiang Marcus Tan, Penny Chong, Jiamei Sun +3
Few-shot classifiers excel under limited training samples, making them useful in applications with sparsely user-provided labels. Their unique relative prediction setup offers oppo…
Split and Expand: An inference-time improvement for Weakly Supervised Cell Instance Segmentation
Lin Geng Foo, Rui En Ho, Jiamei Sun +1
We consider the problem of segmenting cell nuclei instances from Hematoxylin and Eosin (H&E) stains with weak supervision. While most recent works focus on improving the segmentati…
Explanation-Guided Training for Cross-Domain Few-Shot Classification
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek +3
Cross-domain few-shot classification task (CD-FSC) combines few-shot classification with the requirement to generalize across domains represented by datasets. This setup faces chal…
Explain and Improve: LRP-Inference Fine-Tuning for Image Captioning Models
Jiamei Sun, Sebastian Lapuschkin, Wojciech Samek +1
This paper analyzes the predictions of image captioning models with attention mechanisms beyond visualizing the attention itself. We develop variants of layer-wise relevance propag…