Hichem Frigui

Fadoua Khmaissia, Hichem Frigui

We propose a new strategy to improve the accuracy and robustness of image classification. First, we train a baseline CNN model. Then, we identify challenging regions in the feature space by identifying all misclassified samples, and correctly classified samples with low confidence values. These samples are then used to train a Variational AutoEncoder (VAE). Next, the VAE is used to generate synthetic images. Finally, the generated synthetic images are used in conjunction with the original labeled images to train a new model in a semi-supervised fashion. Empirical results on benchmark datasets such as STL10 and CIFAR-100 show that the synthetically generated samples can further diversify the training data, leading to improvement in image classification in comparison with the fully supervised baseline approaches using only the available data.

Amine Ben Khalifa, Hichem Frigui

Fuzzy logic is a powerful tool to model knowledge uncertainty, measurements imprecision, and vagueness. However, there is another type of vagueness that arises when data have multiple forms of expression that fuzzy logic does not address quite well. This is the case for multiple instance learning problems (MIL). In MIL, an object is represented by a collection of instances, called a bag. A bag is labeled negative if all of its instances are negative, and positive if at least one of its instances is positive. Positive bags encode ambiguity since the instances themselves are not labeled. In this paper, we introduce fuzzy inference systems and neural networks designed to handle bags of instances as input and capable of learning from ambiguously labeled data. First, we introduce the Multiple Instance Sugeno style fuzzy inference (MI-Sugeno) that extends the standard Sugeno style inference to handle reasoning with multiple instances. Second, we use MI-Sugeno to define and develop Multiple Instance Adaptive Neuro Fuzzy Inference System (MI-ANFIS). We expand the architecture of the standard ANFIS to allow reasoning with bags and derive a learning algorithm using backpropagation to identify the premise and consequent parameters of the network. The proposed inference system is tested and validated using synthetic and benchmark datasets suitable for MIL problems. We also apply the proposed MI-ANFIS to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.