Learning from noisy labels with deep neural networks: A survey
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 …
amounts of big data. However, the quality of data labels is a concern because of the lack of …
Deep learning with noisy labels: Exploring techniques and remedies in medical image analysis
Supervised training of deep learning models requires large labeled datasets. There is a
growing interest in obtaining such datasets for medical image analysis applications …
growing interest in obtaining such datasets for medical image analysis applications …
Deep long-tailed learning: A survey
Deep long-tailed learning, one of the most challenging problems in visual recognition, aims
to train well-performing deep models from a large number of images that follow a long-tailed …
to train well-performing deep models from a large number of images that follow a long-tailed …
Part-based pseudo label refinement for unsupervised person re-identification
Unsupervised person re-identification (re-ID) aims at learning discriminative representations
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …
for person retrieval from unlabeled data. Recent techniques accomplish this task by using …
Selective-supervised contrastive learning with noisy labels
S Li, X ** the good and removing the bad momentum causal effect
As the class size grows, maintaining a balanced dataset across many classes is challenging
because the data are long-tailed in nature; it is even impossible when the sample-of-interest …
because the data are long-tailed in nature; it is even impossible when the sample-of-interest …
Balanced meta-softmax for long-tailed visual recognition
Deep classifiers have achieved great success in visual recognition. However, real-world
data is long-tailed by nature, leading to the mismatch between training and testing …
data is long-tailed by nature, leading to the mismatch between training and testing …
Meta-learning in neural networks: A survey
The field of meta-learning, or learning-to-learn, has seen a dramatic rise in interest in recent
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …
years. Contrary to conventional approaches to AI where tasks are solved from scratch using …