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Multimodal co-learning: Challenges, applications with datasets, recent advances and future directions
Multimodal deep learning systems that employ multiple modalities like text, image, audio,
video, etc., are showing better performance than individual modalities (ie, unimodal) …
video, etc., are showing better performance than individual modalities (ie, unimodal) …
[HTML][HTML] Machine learning of Raman spectroscopy data for classifying cancers: a review of the recent literature
Raman Spectroscopy has long been anticipated to augment clinical decision making, such
as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far …
as classifying oncological samples. Unfortunately, the complexity of Raman data has thus far …
Image classification with deep learning in the presence of noisy labels: A survey
Image classification systems recently made a giant leap with the advancement of deep
neural networks. However, these systems require an excessive amount of labeled data to be …
neural networks. However, these systems require an excessive amount of labeled data to be …
Gradient descent with early stop** is provably robust to label noise for overparameterized neural networks
Modern neural networks are typically trained in an over-parameterized regime where the
parameters of the model far exceed the size of the training data. Such neural networks in …
parameters of the model far exceed the size of the training data. Such neural networks in …
Training deep neural-networks using a noise adaptation layer
The availability of large datsets has enabled neural networks to achieve impressive
recognition results. However, the presence of inaccurate class labels is known to deteriorate …
recognition results. However, the presence of inaccurate class labels is known to deteriorate …
Learning with bad training data via iterative trimmed loss minimization
In this paper, we study a simple and generic framework to tackle the problem of learning
model parameters when a fraction of the training samples are corrupted. Our approach is …
model parameters when a fraction of the training samples are corrupted. Our approach is …
Pushing on text readability assessment: A transformer meets handcrafted linguistic features
[HTML][HTML] Data augmentation for audio-visual emotion recognition with an efficient multimodal conditional GAN
Audio-visual emotion recognition is the research of identifying human emotional states by
combining the audio modality and the visual modality simultaneously, which plays an …
combining the audio modality and the visual modality simultaneously, which plays an …
Deep learning with noisy labels in medical prediction problems: a sco** review
Objectives Medical research faces substantial challenges from noisy labels attributed to
factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of …
factors like inter-expert variability and machine-extracted labels. Despite this, the adoption of …