Several image processing tasks, such as image classification and object detection, have
been significantly improved using Convolutional Neural Networks (CNN). Like ResNet and …

Deep learning has achieved remarkable success in numerous domains with help from large
amounts of big data. However, the quality of data labels is a concern because of the lack of …

Abstract Noisy label Facial Expression Recognition (FER) is more challenging than
traditional noisy label classification tasks due to the inter-class similarity and the annotation …

Abstract Model heterogeneous federated learning is a challenging task since each client
independently designs its own model. Due to the annotation difficulty and free-riding …

Existing research on learning with noisy labels mainly focuses on synthetic label noise.
Synthetic noise, though has clean structures which greatly enabled statistical analyses, often …

Existing studies indicate that deep neural networks (DNNs) can eventually memorize the
label noise. We observe that the memorization strength of DNNs towards each instance is …

Human-annotated labels are often prone to noise, and the presence of such noise will
degrade the performance of the resulting deep neural network (DNN) models. Much of the …

Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels
usually results in inferior model performance. Existing state-of-the-art methods primarily …

The sample selection approach is popular in learning with noisy labels. The state-of-the-art
methods train two deep networks simultaneously for sample selection, which aims to employ …

Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …