Search Results for author:
Found 9 papers, 5 papers with code
Recognizing Handwritten Mathematical Expressions of Vertical Addition and Subtraction
We also proposed a transcription method to map the bounding boxes from the object detection stage to a mathematical expression in the LATEX markup sequence.
Improving Mass Detection in Mammography Images: A Study of Weakly Supervised Learning and Class Activation Map Methods
This work presents a study that explores and compares different activation maps in conjunction with state-of-the-art methods for weakly supervised training in mammography images.
A Study on the Impact of Data Augmentation for Training Convolutional Neural Networks in the Presence of Noisy Labels
Label noise is common in large real-world datasets, and its presence harms the training process of deep neural networks.
PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels
The most competitive noisy label learning methods rely on an unsupervised classification of clean and noisy samples, where samples classified as noisy are re-labelled and "MixMatched" with the clean samples.
Ranked #1 on
Image Classification with Label Noise
on CIFAR-100
Image Classification with Label Noise
Learning with noisy labels
LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
Deep neural network models are robust to a limited amount of label noise, but their ability to memorise noisy labels in high noise rate problems is still an open issue.
Ranked #4 on
Image Classification
on Food-101N
Self-supervised Mean Teacher for Semi-supervised Chest X-ray Classification
In this paper, we propose Self-supervised Mean Teacher for Semi-supervised (S$^2$MTS$^2$) learning that combines self-supervised mean-teacher pre-training with semi-supervised fine-tuning.
A Survey on Deep Learning with Noisy Labels: How to train your model when you cannot trust on the annotations?
As deep learning models depend on correctly labeled data sets and label correctness is difficult to guarantee, it is crucial to consider the presence of noisy labels for deep learning training.
MyFood: A Food Segmentation and Classification System to Aid Nutritional Monitoring
This work presents the development of an intelligent system that classifies and segments food presented in images to help the automatic monitoring of user diet and nutritional intake.
EvidentialMix: Learning with Combined Open-set and Closed-set Noisy Labels
In this work, we study a new variant of the noisy label problem that combines the open-set and closed-set noisy labels, and introduce a benchmark evaluation to assess the performance of training algorithms under this setup.
