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Understanding deep learning techniques for recognition of human emotions using facial expressions: A comprehensive survey
Emotion recognition plays a significant role in cognitive psychology research. However,
measuring emotions is a challenging task. Thus, several approaches have been designed …
measuring emotions is a challenging task. Thus, several approaches have been designed …
An overview of deep semi-supervised learning
Deep neural networks demonstrated their ability to provide remarkable performances on a
wide range of supervised learning tasks (eg, image classification) when trained on extensive …
wide range of supervised learning tasks (eg, image classification) when trained on extensive …
Disc: Learning from noisy labels via dynamic instance-specific selection and correction
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 …
label noise. We observe that the memorization strength of DNNs towards each instance is …
Early-learning regularization prevents memorization of noisy labels
We propose a novel framework to perform classification via deep learning in the presence of
noisy annotations. When trained on noisy labels, deep neural networks have been observed …
noisy annotations. When trained on noisy labels, deep neural networks have been observed …
Suppressing uncertainties for large-scale facial expression recognition
Annotating a qualitative large-scale facial expression dataset is extremely difficult due to the
uncertainties caused by ambiguous facial expressions, low-quality facial images, and the …
uncertainties caused by ambiguous facial expressions, low-quality facial images, and the …
Normalized loss functions for deep learning with noisy labels
Robust loss functions are essential for training accurate deep neural networks (DNNs) in the
presence of noisy (incorrect) labels. It has been shown that the commonly used Cross …
presence of noisy (incorrect) labels. It has been shown that the commonly used Cross …
Self-training with noisy student improves imagenet classification
We present a simple self-training method that achieves 88.4% top-1 accuracy on ImageNet,
which is 2.0% better than the state-of-the-art model that requires 3.5 B weakly labeled …
which is 2.0% better than the state-of-the-art model that requires 3.5 B weakly labeled …
Dividemix: Learning with noisy labels as semi-supervised learning
Deep neural networks are known to be annotation-hungry. Numerous efforts have been
devoted to reducing the annotation cost when learning with deep networks. Two prominent …
devoted to reducing the annotation cost when learning with deep networks. Two prominent …
Symmetric cross entropy for robust learning with noisy labels
Training accurate deep neural networks (DNNs) in the presence of noisy labels is an
important and challenging task. Though a number of approaches have been proposed for …
important and challenging task. Though a number of approaches have been proposed for …
CancerGPT for few shot drug pair synergy prediction using large pretrained language models
Large language models (LLMs) have been shown to have significant potential in few-shot
learning across various fields, even with minimal training data. However, their ability to …
learning across various fields, even with minimal training data. However, their ability to …