no code implementations • 2 Jul 2023 • Sen Pei
Out-of-distribution (OOD) detection empowers the model trained on the closed image set to identify unknown data in the open world.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 3 Mar 2023 • Sen Pei, Jingya Yu, Qi Chen, Wozhou He
In this paper, we investigate a novel and practical problem, namely audio beat matching (ABM), which aims to recommend the proper transition time stamps based on the background music.
no code implementations • 17 Jan 2023 • Sen Pei, Jiaxi Sun, Richard Yi Da Xu, Bin Fan, Shiming Xiang, Gaofeng Meng
Generally, existing approaches in dealing with out-of-distribution (OOD) detection mainly focus on the statistical difference between the features of OOD and in-distribution (ID) data extracted by the classifiers.
1 code implementation • 12 Sep 2022 • Sen Pei, Shixiong Xu, Xiaojie Jin
However, most VHD methods are based on the closed world assumption, i. e., a fixed number of highlight categories is defined in advance and all training data are available beforehand.
1 code implementation • 25 Jul 2022 • Sen Pei, Jiaxi Sun, Richard Yi Da Xu, Shiming Xiang, Gaofeng Meng
PoER helps the neural networks to capture label-related features which contain the domain information first in shallow layers and then distills the label-discriminative representations out progressively, enforcing the neural networks to be aware of the characteristic of objects and background which is vital to the generation of domain-invariant features.
no code implementations • 21 May 2022 • Sen Pei, Jiaxi Sun, Xiaopeng Zhang, Gaofeng Meng
Recent studies show that the deep neural networks (DNNs) have achieved great success in various tasks.
no code implementations • 9 Feb 2022 • Renquan Zhang, Yu Wang, Zheng Lv, Sen Pei
We generate counterfactual simulations to estimate effectiveness of quarantine measures.
no code implementations • 22 Dec 2021 • Sen Pei, Xin Zhang, Richard Yida Xu, Gaofeng Meng
This paper focuses on the problem of detecting out-of-distribution (ood) samples with neural nets.
Out-of-Distribution Detection Out of Distribution (OOD) Detection
no code implementations • 5 Aug 2021 • Sen Pei, Richard Yi Da Xu, Shiming Xiang, Gaofeng Meng
We compare the proposed method with Unrolled GAN (Metz et al. 2016), BourGAN (Xiao, Zhong, and Zheng 2018), PacGAN (Lin et al. 2018), VEEGAN (Srivastava et al. 2017) and ALI (Dumoulin et al. 2016) on 2D synthetic dataset, and results show that the diversity penalty module can help GAN capture much more modes of the data distribution.