no code implementations • 20 Dec 2023 • Ziwei Zhang, Mengtao Zhu, Jiabin Liu, Yunjie Li, Shafei Wang
To enhance distinguishability, we design Class Conditional Vectors (CCVs) to match the latent representations extracted from input samples, achieving perfect reconstruction for known samples while yielding poor results for unknown ones.
no code implementations • 1 Oct 2021 • Jiabin Liu, Zheng Wei, Zhengpin Li, Xiaojun Mao, Jian Wang, Zhongyu Wei, Qi Zhang
In this work, we propose a novel and general self-adaptive module, the Self-adaptive Attention Module (SAM), which adjusts the selection bias by capturing contextual information based on its representation.
1 code implementation • 10 Jun 2021 • Hongwei Wen, Jingyi Cui, Hanyuan Hang, Jiabin Liu, Yisen Wang, Zhouchen Lin
As an important branch of weakly supervised learning, partial label learning deals with data where each instance is assigned with a set of candidate labels, whereas only one of them is true.
no code implementations • 22 May 2021 • Jiabin Liu, Bo wang, Xin Shen, Zhiquan Qi, Yingjie Tian
Learning from label proportions (LLP) aims at learning an instance-level classifier with label proportions in grouped training data.
no code implementations • 1 Jan 2021 • Jiabin Liu, Hanyuan Hang, Bo wang, Xin Shen, Zhouchen Lin
Learning from label proportions (LLP), where the training data are arranged in form of groups with only label proportions provided instead of the exact labels, is an important weakly supervised learning paradigm in machine learning.
no code implementations • ICLR 2020 • Yong Shi, Biao Li, Bo wang, Zhiquan Qi, Jiabin Liu, Fan Meng
Super Resolution (SR) is a fundamental and important low-level computer vision (CV) task.
1 code implementation • 24 Sep 2019 • Biao Li, Jiabin Liu, Bo Wang, Zhiquan Qi, Yong Shi
Deep learning (DL) architectures for superresolution (SR) normally contain tremendous parameters, which has been regarded as the crucial advantage for obtaining satisfying performance.
1 code implementation • NeurIPS 2019 • Jiabin Liu, Bo wang, Zhiquan Qi, Yingjie Tian, Yong Shi
In this paper, we leverage generative adversarial networks (GANs) to derive an effective algorithm LLP-GAN for learning from label proportions (LLP), where only the bag-level proportional information in labels is available.