8 papers
Bridging Generative and Discriminative Noisy-Label Learning via Direction-Agnostic EM Formulation
Fengbei Liu, Chong Wang, Yuanhong Chen +2
Although noisy-label learning is often approached with discriminative methods for simplicity and speed, generative modeling offers a principled alternative by capturing the joint m…
Maximising the Utility of Validation Sets for Imbalanced Noisy-label Meta-learning
Dung Anh Hoang, Cuong Nguyen, Belagiannis Vasileios +2
Meta-learning is an effective method to handle imbalanced and noisy-label learning, but it depends on a validation set containing randomly selected, manually labelled and balanced…
Translation Consistent Semi-supervised Segmentation for 3D Medical Images
Yuyuan Liu, Yu Tian, Chong Wang +4
3D medical image segmentation methods have been successful, but their dependence on large amounts of voxel-level annotated data is a disadvantage that needs to be addressed given t…
Kernel Adversarial Learning for Real-world Image Super-resolution
Hu Wang, Congbo Ma, Jianpeng Zhang +2
Current deep image super-resolution (SR) approaches aim to restore high-resolution images from down-sampled images or by assuming degradation from simple Gaussian kernels and addit…
Unraveling Instance Associations: A Closer Look for Audio-Visual Segmentation
Yuanhong Chen, Yuyuan Liu, Hu Wang +4
Audio-visual segmentation (AVS) is a challenging task that involves accurately segmenting sounding objects based on audio-visual cues. The effectiveness of audio-visual learning cr…
Instance-dependent Noisy-label Learning with Graphical Model Based Noise-rate Estimation
Arpit Garg, Cuong Nguyen, Rafael Felix +2
Deep learning faces a formidable challenge when handling noisy labels, as models tend to overfit samples affected by label noise. This challenge is further compounded by the presen…