6 accepted papers
Recent advances in model compression have highlighted the potential of low-bit precision techniques, with Binary Neural Networks (BNNs) attracting attention for their extreme efficiency. However, extreme quantization in BNNs limits representational capacity and destabilizes training, posing signific
Self-supervised learning (SSL) methods based on the instance discrimination tasks with InfoNCE have achieved remarkable success. Despite their success, SSL models often struggle to generate effective representations for unseen-domain data. To address this issue, research on unsupervised domain gener…
"Semi-supervised learning is a learning method that uses both labeled and unlabeled samples to improve the performance of the model while reducing labeling costs. When there were tens to hundreds of labeled samples, semi-supervised learning methods showed good performance, but most of them showed po…
"Single Positive Multi-Label Learning (SPML) is a method for a scarcely annotated setting, in which each image is assigned only one positive label while the other labels remain unannotated. Most approaches for SPML assume unannotated labels as negatives (“Assumed Negative”, AN). However, with this a…
Deep Neural Networks (DNNs) have demonstrated remarkable performance across diverse domains and tasks with large-scale datasets. To reduce labeling costs for large-scale datasets semi-automated and crowdsourcing labeling methods are developed but their labels are inevitably noisy. Learning with Nois…
Semi-supervised learning (SSL) is a method to make better models using a large number of easily accessible unlabeled data along with a small number of labeled data obtained at a high cost. Most of existing SSL studies focus on the cases where sufficient amount of labeled samples are available, tens…