200 citations · 296 across the 6 of their papers we have counts for
6 papers
Contrastive Regularization for Semi-Supervised Learning
Doyup Lee, Sungwoong Kim, Ildoo Kim +3
Consistency regularization on label predictions becomes a fundamental technique in semi-supervised learning, but it still requires a large number of training iterations for high pe…
Regularizing Attention Networks for Anomaly Detection in Visual Question Answering
Doyup Lee, Yeongjae Cheon, Wook-Shin Han
For stability and reliability of real-world applications, the robustness of DNNs in unimodal tasks has been evaluated. However, few studies consider abnormal situations that a visu…
Soft Labeling Affects Out-of-Distribution Detection of Deep Neural Networks
Doyup Lee, Yeongjae Cheon
Soft labeling becomes a common output regularization for generalization and model compression of deep neural networks. However, the effect of soft labeling on out-of-distribution (…
Demand Forecasting from Spatiotemporal Data with Graph Networks and Temporal-Guided Embedding
Doyup Lee, Suehun Jung, Yeongjae Cheon +2
Short-term demand forecasting models commonly combine convolutional and recurrent layers to extract complex spatiotemporal patterns in data. Long-term histories are also used to co…
PVANet: Lightweight Deep Neural Networks for Real-time Object Detection
Sanghoon Hong, Byungseok Roh, Kye-Hyeon Kim +2
In object detection, reducing computational cost is as important as improving accuracy for most practical usages. This paper proposes a novel network structure, which is an order o…
PVANET: Deep but Lightweight Neural Networks for Real-time Object Detection
Kye-Hyeon Kim, Sanghoon Hong, Byungseok Roh +2
This paper presents how we can achieve the state-of-the-art accuracy in multi-category object detection task while minimizing the computational cost by adapting and combining recen…