Abstract Machine learning methods strive to acquire a robust model during the training
process that can effectively generalize to test samples, even in the presence of distribution …

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 …

Large language models (LLMs) have been recently leveraged as training data generators
for various natural language processing (NLP) tasks. While previous research has explored …

Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …

Although effective deepfake detection models have been developed in recent years recent
studies have revealed that these models can result in unfair performance disparities among …

Bounding box (bbox) regression is a fundamental task in computer vision. So far, the most
commonly used loss functions for bbox regression are the Intersection over Union (IoU) loss …

Abstract Standard Unsupervised Domain Adaptation (UDA) methods assume the availability
of both source and target data during the adaptation. In this work, we investigate Source-free …

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 …

Learning from noisy data is a challenging task that significantly degenerates the model
performance. In this paper, we present TCL, a novel twin contrastive learning model to learn …

Self-training is a competitive approach in domain adaptive segmentation, which trains the
network with the pseudo labels on the target domain. However inevitably, the pseudo labels …