1 code implementation • 4 Apr 2024 • Tianqi Li, Guansong Pang, Xiao Bai, Wenjun Miao, Jin Zheng
Existing prompt learning methods have shown certain capabilities in Out-of-Distribution (OOD) detection, but the lack of OOD images in the target dataset in their training can lead to mismatches between OOD images and In-Distribution (ID) categories, resulting in a high false positive rate.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
1 code implementation • 27 Feb 2024 • ZiCheng Zhang, Ruobing Zheng, Ziwen Liu, Congying Han, Tianqi Li, Meng Wang, Tiande Guo, Jingdong Chen, Bonan Li, Ming Yang
Recent works in implicit representations, such as Neural Radiance Fields (NeRF), have advanced the generation of realistic and animatable head avatars from video sequences.
1 code implementation • 17 Dec 2023 • Wenjun Miao, Guansong Pang, Tianqi Li, Xiao Bai, Jin Zheng
To this end, we introduce a novel calibrated outlier class learning (COCL) approach, in which 1) a debiased large margin learning method is introduced in the outlier class learning to distinguish OOD samples from both head and tail classes in the representation space and 2) an outlier-class-aware logit calibration method is defined to enhance the long-tailed classification confidence.
1 code implementation • 7 Jul 2023 • Tianqi Li, Guansong Pang, Xiao Bai, Jin Zheng, Lei Zhou, Xin Ning
Open-Set Recognition (OSR) is dedicated to addressing the unknown class issue, but existing OSR methods are not designed to model the semantic information of the unseen classes.
no code implementations • 15 Jun 2021 • Jiefeng Chen, Yang Guo, Xi Wu, Tianqi Li, Qicheng Lao, YIngyu Liang, Somesh Jha
Compared to traditional "test-time" defenses, these defense mechanisms "dynamically retrain" the model based on test time input via transductive learning; and theoretically, attacking these defenses boils down to bilevel optimization, which seems to raise the difficulty for adaptive attacks.
no code implementations • 1 Jan 2021 • Xi Wu, Yang Guo, Tianqi Li, Jiefeng Chen, Qicheng Lao, YIngyu Liang, Somesh Jha
On the positive side, we show that, if one is allowed to access the training data, then Domain Adversarial Neural Networks (${\sf DANN}$), an algorithm designed for unsupervised domain adaptation, can provide nontrivial robustness in the test-time maximin threat model against strong transfer attacks and adaptive fixed point attacks.
no code implementations • 8 Nov 2018 • Naoki Kato, Tianqi Li, Kohei Nishino, Yusuke Uchida
If a model is trained with data including such missing labels, the output of the model for the location, even though it is correct, is penalized as a false positive, which is likely to cause negative effects on the performance of the model.
no code implementations • 6 Sep 2018 • Koichi Hamada, Kentaro Tachibana, Tianqi Li, Hiroto Honda, Yusuke Uchida
Our method tackles the limitations by progressively increasing the resolution of both generated images and structural conditions during training.