6 citations · 15 across the 9 of their papers we have counts for
9 papers
Cost-Sensitive Conformal Training with Provably Controllable Learning Bounds
Xuesong Jia, Yuanjie Shi, Ziquan Liu +2
Conformal prediction (CP) is a general framework to quantify the predictive uncertainty of machine learning models that uses a set prediction to include the true label with a valid…
TWINS: A Fine-Tuning Framework for Improved Transferability of Adversarial Robustness and Generalization
Ziquan Liu, Yi Xu, Xiangyang Ji +1
Recent years have seen the ever-increasing importance of pre-trained models and their downstream training in deep learning research and applications. At the same time, the defense…
HCSC: Hierarchical Contrastive Selective Coding
Yuanfan Guo, Minghao Xu, Jiawen Li +4
Hierarchical semantic structures naturally exist in an image dataset, in which several semantically relevant image clusters can be further integrated into a larger cluster with coa…
Boosting Unsupervised Domain Adaptation with Soft Pseudo-label and Curriculum Learning
Shengjia Zhang, Tiancheng Lin, Yi Xu
By leveraging data from a fully labeled source domain, unsupervised domain adaptation (UDA) improves classification performance on an unlabeled target domain through explicit discr…
SLAP: Self-supervised Anomaly Detection with Adversarial Perturbation
Yizhou Wang, Can Qin, Rongzhe Wei +3
Anomaly detection is a fundamental yet challenging problem in machine learning due to the lack of label information. In this work, we propose a novel and powerful framework, dubbed…
MLIM: Vision-and-Language Model Pre-training with Masked Language and Image Modeling
Tarik Arici, Mehmet Saygin Seyfioglu, Tal Neiman +5
Vision-and-Language Pre-training (VLP) improves model performance for downstream tasks that require image and text inputs. Current VLP approaches differ on (i) model architecture (…
Dynamic Dual Sampling Module for Fine-Grained Semantic Segmentation
Chen Shi, Xiangtai Li, Yanran Wu +2
Representation of semantic context and local details is the essential issue for building modern semantic segmentation models. However, the interrelationship between semantic contex…
Evaluating and Boosting Uncertainty Quantification in Classification
Xiaoyang Huang, Jiancheng Yang, Linguo Li +3
Emergence of artificial intelligence techniques in biomedical applications urges the researchers to pay more attention on the uncertainty quantification (UQ) in machine-assisted me…
Disentangled Deep Autoencoding Regularization for Robust Image Classification
Zhenyu Duan, Martin Renqiang Min, Li Erran Li +3
In spite of achieving revolutionary successes in machine learning, deep convolutional neural networks have been recently found to be vulnerable to adversarial attacks and difficult…