1.1k citations · 2.7k across the 92 of their papers we have counts for
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Task Weighting in Meta-learning with Trajectory Optimisation
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
Developing meta-learning algorithms that are un-biased toward a subset of training tasks often requires hand-designed criteria to weight tasks, potentially resulting in sub-optimal…
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…
Mixup-based Deep Metric Learning Approaches for Incomplete Supervision
Luiz H. Buris, Daniel C. G. Pedronette, Joao P. Papa +3
Deep learning architectures have achieved promising results in different areas (e.g., medicine, agriculture, and security). However, using those powerful techniques in many real ap…
Probabilistic task modelling for meta-learning
Cuong C. Nguyen, Thanh-Toan Do, Gustavo Carneiro
We propose probabilistic task modelling -- a generative probabilistic model for collections of tasks used in meta-learning. The proposed model combines variational auto-encoding an…
Similarity of Classification Tasks
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
Recent advances in meta-learning has led to remarkable performances on several few-shot learning benchmarks. However, such success often ignores the similarity between training and…
Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)
Hoang Son Le, Rini Akmeliawati, Gustavo Carneiro
Domain generalisation (DG) methods address the problem of domain shift, when there is a mismatch between the distributions of training and target domains. Data augmentation approac…
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…
PAC-Bayes meta-learning with implicit task-specific posteriors
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
We introduce a new and rigorously-formulated PAC-Bayes meta-learning algorithm that solves few-shot learning. Our proposed method extends the PAC-Bayes framework from a single task…
Hidden Stratification Causes Clinically Meaningful Failures in Machine Learning for Medical Imaging
Luke Oakden-Rayner, Jared Dunnmon, Gustavo Carneiro +1
Machine learning models for medical image analysis often suffer from poor performance on important subsets of a population that are not identified during training or testing. For e…
Uncertainty in Model-Agnostic Meta-Learning using Variational Inference
Cuong Nguyen, Thanh-Toan Do, Gustavo Carneiro
We introduce a new, rigorously-formulated Bayesian meta-learning algorithm that learns a probability distribution of model parameter prior for few-shot learning. The proposed algor…
Bayesian Generative Active Deep Learning
Toan Tran, Thanh-Toan Do, Ian Reid +1
Deep learning models have demonstrated outstanding performance in several problems, but their training process tends to require immense amounts of computational and human resources…