204 citations · 402 across the 14 of their papers we have counts for
14 papers
Explaining How Deep Neural Networks Forget by Deep Visualization
Giang Nguyen, Shuan Chen, Tae Joon Jun +1
Explaining the behaviors of deep neural networks, usually considered as black boxes, is critical especially when they are now being adopted over diverse aspects of human life. Taki…
Smart Inference for Multidigit Convolutional Neural Network based Barcode Decoding
Thao Do, Yalew Tolcha, Tae Joon Jun +1
Barcodes are ubiquitous and have been used in most of critical daily activities for decades. However, most of traditional decoders require well-founded barcode under a relatively s…
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…