CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances J Tack*, S Mo*, J Jeong, J Shin Advances in Neural Information Processing Systems, 2020 | 545 | 2020 |
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs S Mo, M Cho, J Shin CVPR AI for Content Creation Workshop, 2020 | 224 | 2020 |
InstaGAN: Instance-aware Image-to-Image Translation S Mo, M Cho, J Shin International Conference on Learning Representations, 2019 | 199 | 2019 |
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks S Yu*, J Tack*, S Mo*, H Kim, J Kim, JW Ha, J Shin International Conference on Learning Representations, 2022 | 156 | 2022 |
Layer-adaptive sparsity for the Magnitude-based Pruning J Lee, S Park, S Mo, S Ahn, J Shin International Conference on Learning Representations, 2021 | 119 | 2021 |
Lookahead: A Far-Sighted Alternative of Magnitude-based Pruning S Park*, J Lee*, S Mo, J Shin International Conference on Learning Representations, 2020 | 99 | 2020 |
Deep neural network based electrical impedance tomographic sensing methodology for large-area robotic tactile sensing H Park, K Park, S Mo, J Kim IEEE Transactions on Robotics 37 (5), 1570-1583, 2021 | 44 | 2021 |
Object-aware Contrastive Learning for Debiased Scene Representation S Mo*, H Kang*, K Sohn, CL Li, J Shin Advances in Neural Information Processing Systems, 2021 | 37 | 2021 |
MASKER: Masked Keyword Regularization for Reliable Text Classification SJ Moon*, S Mo*, K Lee, J Lee, J Shin AAAI Conference on Artificial Intelligence, 2021 | 32 | 2021 |
Deep Neural Network Approach in Electrical Impedance Tomography-based Real-time Soft Tactile Sensor H Park, H Lee, K Park, S Mo, J Kim 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2019 | 26 | 2019 |
Contextual multi-armed bandits under feature uncertainty S Yun, JH Nam, S Mo, J Shin Los Alamos National Lab.(LANL), Los Alamos, NM (United States), 2017 | 19 | 2017 |
Bias-to-text: Debiasing unknown visual biases through language interpretation Y Kim*, S Mo*, M Kim, K Lee, J Lee, J Shin arXiv preprint arXiv:2301.11104 2, 2023 | 17* | 2023 |
Mining GOLD Samples for Conditional GANs S Mo, C Kim, S Kim, M Cho, J Shin Advances in Neural Information Processing Systems, 2019 | 15 | 2019 |
Abstract Reasoning via Logic-guided Generation S Yu, S Mo, S Ahn, J Shin ICML Self-Supervised Learning for Reasoning and Perception Workshop, 2021 | 7 | 2021 |
RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data S Mo, JC Su, CY Ma, M Assran, I Misra, L Yu, S Bell International Conference on Learning Representations, 2023 | 6 | 2023 |
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions S Mo, M Kim, K Lee, J Shin Advances in Neural Information Processing Systems, 2023 | 4 | 2023 |
OAMixer: Object-aware Mixing Layer for Vision Transformers H Kang*, S Mo*, J Shin CVPR Transformers for Vision Workshop, 2022 | 4* | 2022 |
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling J Nam, S Mo, J Lee, J Shin Transactions on Machine Learning Research, 2023 | 3 | 2023 |
Learning Hierarchical Image Segmentation For Recognition and By Recognition TW Ke*, S Mo*, XY Stella International Conference on Learning Representations, 2024 | 2* | 2024 |
SuRe: Improving Open-domain Question Answering of LLMs via Summarized Retrieval J Kim, J Nam, S Mo, J Park, SW Lee, M Seo, JW Ha, J Shin International Conference on Learning Representations, 2024 | 2 | 2024 |