13 citations · 27 across the 4 of their papers we have counts for
7 papers
Applying Tensor Decomposition to image for Robustness against Adversarial Attack
Seungju Cho, Tae Joon Jun, Mingu Kang +1
Nowadays the deep learning technology is growing faster and shows dramatic performance in computer vision areas. However, it turns out a deep learning based model is highly vulnera…
Unbalanced GANs: Pre-training the Generator of Generative Adversarial Network using Variational Autoencoder
Hyungrok Ham, Tae Joon Jun, Daeyoung Kim
We propose Unbalanced GANs, which pre-trains the generator of the generative adversarial network (GAN) using variational autoencoder (VAE). We guarantee the stable training of the…
Dissecting Catastrophic Forgetting in Continual Learning by Deep Visualization
Giang Nguyen, Shuan Chen, Thao Do +3
Interpreting the behaviors of Deep Neural Networks (usually considered as a black box) is critical especially when they are now being widely adopted over diverse aspects of human l…
ContCap: A scalable framework for continual image captioning
Giang Nguyen, Tae Joon Jun, Trung Tran +2
While advanced image captioning systems are increasingly describing images coherently and exactly, recent progress in continual learning allows deep learning models to avoid catast…
DAPAS : Denoising Autoencoder to Prevent Adversarial attack in Semantic Segmentation
Seungju Cho, Tae Joon Jun, Byungsoo Oh +1
Nowadays, Deep learning techniques show dramatic performance on computer vision area, and they even outperform human. But it is also vulnerable to some small perturbation called an…
TRk-CNN: Transferable Ranking-CNN for image classification of glaucoma, glaucoma suspect, and normal eyes
Tae Joon Jun, Youngsub Eom, Dohyeun Kim +4
In this paper, we proposed Transferable Ranking Convolutional Neural Network (TRk-CNN) that can be effectively applied when the classes of images to be classified show a high corre…