Search Results for author:
Found 6 papers, 2 papers with code
PNP: Robust Learning From Noisy Labels by Probabilistic Noise Prediction
Label noise has been a practical challenge in deep learning due to the strong capability of deep neural networks in fitting all training data.
Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach
Learning from the web can ease the extreme dependence of deep learning on large-scale manually labeled datasets.
Jo-SRC: A Contrastive Approach for Combating Noisy Labels
Due to the memorization effect in Deep Neural Networks (DNNs), training with noisy labels usually results in inferior model performance.
Salvage Reusable Samples from Noisy Data for Robust Learning
To this end, we propose a certainty-based reusable sample selection and correction approach, termed as CRSSC, for coping with label noise in training deep FG models with web images.
Dynamically Visual Disambiguation of Keyword-based Image Search
To address this issue, we present an adaptive multi-model framework that resolves polysemy by visual disambiguation.
Road Segmentation with Image-LiDAR Data Fusion
Robust road segmentation is a key challenge in self-driving research.
