63 citations · 83 across the 5 of their papers we have counts for
7 papers
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels
Filipe R. Cordeiro, Vasileios Belagiannis, Ian Reid +1
The most competitive noisy label learning methods rely on an unsupervised classification of clean and noisy samples, where samples classified as noisy are re-labelled and "MixMatch…
Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
Fengbei Liu, Yu Tian, Filipe R. Cordeiro +3
The training of deep learning models generally requires a large amount of annotated data for effective convergence and generalisation. However, obtaining high-quality annotations i…
MyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring
Charles N. C. Freitas, Filipe R. Cordeiro, Valmir Macario
The absence of food monitoring has contributed significantly to the increase in the population's weight. Due to the lack of time and busy routines, most people do not control and r…
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?
Filipe R. Cordeiro, Gustavo Carneiro
Noisy Labels are commonly present in data sets automatically collected from the internet, mislabeled by non-specialist annotators, or even specialists in a challenging task, such a…
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
Ragav Sachdeva, Filipe R. Cordeiro, Vasileios Belagiannis +2
The efficacy of deep learning depends on large-scale data sets that have been carefully curated with reliable data acquisition and annotation processes. However, acquiring such lar…
Analysis of supervised and semi-supervised GrowCut applied to segmentation of masses in mammography images
Filipe Rolim Cordeiro, Wellington Pinheiro dos Santos, Abel Guilhermino da Silva Filho
Breast cancer is already one of the most common form of cancer worldwide. Mammography image analysis is still the most effective diagnostic method to promote the early detection of…