3 citations · 14 across the 19 of their papers we have counts for
5 papers · 1 filter
SLIM: Spuriousness Mitigation with Minimal Human Annotations
Xiwei Xuan, Ziquan Deng, Hsuan-Tien Lin +1
Recent studies highlight that deep learning models often learn spurious features mistakenly linked to labels, compromising their reliability in real-world scenarios where such corr…
Score-based Conditional Generation with Fewer Labeled Data by Self-calibrating Classifier Guidance
Paul Kuo-Ming Huang, Si-An Chen, Hsuan-Tien Lin
Score-based generative models (SGMs) are a popular family of deep generative models that achieve leading image generation quality. Early studies extend SGMs to tackle class-conditi…
Semi-Supervised Domain Adaptation with Source Label Adaptation
Yu-Chu Yu, Hsuan-Tien Lin
Semi-Supervised Domain Adaptation (SSDA) involves learning to classify unseen target data with a few labeled and lots of unlabeled target data, along with many labeled source data…
SUNY: A Visual Interpretation Framework for Convolutional Neural Networks from a Necessary and Sufficient Perspective
Xiwei Xuan, Ziquan Deng, Hsuan-Tien Lin +2
Researchers have proposed various methods for visually interpreting the Convolutional Neural Network (CNN) via saliency maps, which include Class-Activation-Map (CAM) based approac…
A Unified View of cGANs with and without Classifiers
Si-An Chen, Chun-Liang Li, Hsuan-Tien Lin
Conditional Generative Adversarial Networks (cGANs) are implicit generative models which allow to sample from class-conditional distributions. Existing cGANs are based on a wide ra…