| Mggan: Solving mode collapse using manifold-guided training D Bang, H Shim Proceedings of the IEEE/CVF international conference on computer vision …, 2021 | 80 | 2021 |
| Improved training of generative adversarial networks using representative features D Bang, H Shim International conference on machine learning, 433-442, 2018 | 40 | 2018 |
| GridMix: Strong regularization through local context mapping K Baek*, D Bang*, H Shim Pattern Recognition 109, 107594, 2021 | 25 | 2021 |
| Discriminator Feature-Based Inference by Recycling the Discriminator of GANs D Bang*, S Kang*, H Shim International Journal of Computer Vision 128 (10), 2436-2458, 2020 | 15* | 2020 |
| Distilling from professors: Enhancing the knowledge distillation of teachers D Bang, J Lee, H Shim Information sciences 576, 743-755, 2021 | 11 | 2021 |
| Editable generative adversarial networks: Generating and editing faces simultaneously K Baek, D Bang, H Shim Computer Vision–ACCV 2018: 14th Asian Conference on Computer Vision, Perth …, 2019 | 7 | 2019 |
| Resembled generative adversarial networks: two domains with similar attributes D Bang, H Shim 29th British Machine Vision Conference, BMVC 2018, 2018 | 5 | 2018 |
| Logit Mixing Training for More Reliable and Accurate Prediction D Bang, K Baek, J Kim, Y Jeon, JH Kim, J Kim, J Lee, H Shim international joint conference on artificial intelligence (IJCAI), 2812-2819, 2022 | 3 | 2022 |
| Logit As Auxiliary Weak-supervision for More Reliable and Accurate Prediction D Bang, Y Jeon, JH Kim, J Kim, H Shim | | 2020 |
| Depth image enhancement using perceptual texture priors D Bang, H Shim Human Vision and Electronic Imaging XX 9394, 452-459, 2015 | | 2015 |
Hyunjung ShimAssociate Professor, KAISTПотвърден имейл адрес: kaist.ac.kr
