3 citations · 4 across the 3 of their papers we have counts for
3 papers
cs.LG2024
Multi-Epoch learning with Data Augmentation for Deep Click-Through Rate Prediction
Zhongxiang Fan, Zhaocheng Liu, Jian Liang +5
This paper investigates the one-epoch overfitting phenomenon in Click-Through Rate (CTR) models, where performance notably declines at the start of the second epoch. Despite extens…
cs.CR2023★ 1 cited
Erase and Repair: An Efficient Box-Free Removal Attack on High-Capacity Deep Hiding
Hangcheng Liu, Tao Xiang, Shangwei Guo +3
Deep hiding, embedding images with others using deep neural networks, has demonstrated impressive efficacy in increasing the message capacity and robustness of secret sharing. In t…
cs.MM2023★ 3 cited
Smaller Is Bigger: Rethinking the Embedding Rate of Deep Hiding
Han Li, Hangcheng Liu, Shangwei Guo +4
Deep hiding, concealing secret information using Deep Neural Networks (DNNs), can significantly increase the embedding rate and improve the efficiency of secret sharing. Existing w…