28 citations · 54 across the 9 of their papers we have counts for
9 papers
AEON: Adaptive Estimation of Instance-Dependent In-Distribution and Out-of-Distribution Label Noise for Robust Learning
Arpit Garg, Cuong Nguyen, Rafael Felix +3
Robust training with noisy labels is a critical challenge in image classification, offering the potential to reduce reliance on costly clean-label datasets. Real-world datasets oft…
Progressive Feature Adjustment for Semi-supervised Learning from Pretrained Models
Hai-Ming Xu, Lingqiao Liu, Hao Chen +2
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 unlab…
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…
PASS: Peer-Agreement based Sample Selection for training with Noisy Labels
Arpit Garg, Cuong Nguyen, Rafael Felix +2
The prevalence of noisy-label samples poses a significant challenge in deep learning, inducing overfitting effects. This has, therefore, motivated the emergence of learning with no…
Instance-Dependent Noisy Label Learning via Graphical Modelling
Arpit Garg, Cuong Nguyen, Rafael Felix +2
Noisy labels are unavoidable yet troublesome in the ecosystem of deep learning because models can easily overfit them. There are many types of label noise, such as symmetric, asymm…
Generalised Zero-Shot Learning with Domain Classification in a Joint Semantic and Visual Space
Rafael Felix, Ben Harwood, Michele Sasdelli +1
Generalised zero-shot learning (GZSL) is a classification problem where the learning stage relies on a set of seen visual classes and the inference stage aims to identify both the…
Generalised Zero-Shot Learning with a Classifier Ensemble over Multi-Modal Embedding Spaces
Rafael Felix, Ben Harwood, Michele Sasdelli +1
Generalised zero-shot learning (GZSL) methods aim to classify previously seen and unseen visual classes by leveraging the semantic information of those classes. In the context of G…
Multi-modal Ensemble Classification for Generalized Zero Shot Learning
Rafael Felix, Michele Sasdelli, Ian Reid +1
Generalized zero shot learning (GZSL) is defined by a training process containing a set of visual samples from seen classes and a set of semantic samples from seen and unseen class…
Multi-modal Cycle-consistent Generalized Zero-Shot Learning
Rafael Felix, B. G. Vijay Kumar, Ian Reid +1
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 a…