no code implementations • 9 Sep 2023 • Hai-Ming Xu, Lingqiao Liu, Hao Chen, Ehsan Abbasnejad, Rafael Felix
As an effective way to alleviate the burden of data annotation, semi-supervised learning (SSL) provides an attractive solution due to its ability to leverage both labeled and unlabeled data to build a predictive model.
no code implementations • 31 May 2023 • Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do, Gustavo Carneiro
To address IDN, Label Noise Learning (LNL) incorporates a sample selection stage to differentiate clean and noisy-label samples.
no code implementations • 20 Mar 2023 • Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do, Gustavo Carneiro
The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects.
1 code implementation • 2 Sep 2022 • Arpit Garg, Cuong Nguyen, Rafael Felix, Thanh-Toan Do, Gustavo Carneiro
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them.
Ranked #1 on Learning with noisy labels on CIFAR-100
no code implementations • 14 Aug 2019 • Rafael Felix, Ben Harwood, Michele Sasdelli, Gustavo Carneiro
Most state-of-the-art GZSL approaches rely on a mapping between latent visual and semantic spaces without considering if a particular sample belongs to the set of seen or unseen classes.
no code implementations • 6 Aug 2019 • Rafael Felix, Ben Harwood, Michele Sasdelli, Gustavo Carneiro
In the context of GZSL, semantic information is non-visual data such as a text description of both seen and unseen classes.
no code implementations • 15 Jan 2019 • Rafael Felix, Michele Sasdelli, Ian Reid, Gustavo Carneiro
In this paper, we mitigate these issues by proposing a new GZSL method based on multi-modal training and testing processes, where the optimization explicitly promotes a balanced classification accuracy between seen and unseen classes.
1 code implementation • ECCV 2018 • Rafael Felix, B. G. Vijay Kumar, Ian Reid, Gustavo Carneiro
In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.
Ranked #5 on Generalized Zero-Shot Learning on SUN Attribute