no code implementations • 30 Nov 2023 • Chong Wang, Yuanhong Chen, Fengbei Liu, Davis James McCarthy, Helen Frazer, Gustavo Carneiro
Such an approach enables the learning of more powerful prototype representations since each learned prototype will own a measure of variability, which naturally reduces the sparsity given the spread of the distribution around each prototype, and we also integrate a prototype diversity objective function into the GMM optimisation to reduce redundancy.
1 code implementation • 6 Apr 2023 • Yuanhong Chen, Yuyuan Liu, Hu Wang, Fengbei Liu, Chong Wang, Helen Frazer, Gustavo Carneiro
We show empirical results that demonstrate the effectiveness of our benchmark.
no code implementations • 31 Jan 2023 • Yuanhong Chen, Yuyuan Liu, Chong Wang, Michael Elliott, Chun Fung Kwok, Carlos Pena-Solorzano, Yu Tian, Fengbei Liu, Helen Frazer, Davis J. McCarthy, Gustavo Carneiro
Given the large size of such datasets, researchers usually face a dilemma with the weakly annotated subset: to not use it or to fully annotate it.
1 code implementation • ICCV 2023 • Chong Wang, Yuyuan Liu, Yuanhong Chen, Fengbei Liu, Yu Tian, Davis J. McCarthy, Helen Frazer, Gustavo Carneiro
Prototypical part network (ProtoPNet) methods have been designed to achieve interpretable classification by associating predictions with a set of training prototypes, which we refer to as trivial prototypes because they are trained to lie far from the classification boundary in the feature space.
Explainable Artificial Intelligence (XAI) Image Classification +1
no code implementations • 26 Sep 2022 • Chong Wang, Yuanhong Chen, Yuyuan Liu, Yu Tian, Fengbei Liu, Davis J. McCarthy, Michael Elliott, Helen Frazer, Gustavo Carneiro
On the other hand, prototype-based models improve interpretability by associating predictions with training image prototypes, but they are less accurate than global models and their prototypes tend to have poor diversity.
1 code implementation • 21 Sep 2022 • Yuanhong Chen, Hu Wang, Chong Wang, Yu Tian, Fengbei Liu, Michael Elliott, Davis J. McCarthy, Helen Frazer, Gustavo Carneiro
When analysing screening mammograms, radiologists can naturally process information across two ipsilateral views of each breast, namely the cranio-caudal (CC) and mediolateral-oblique (MLO) views.