no code implementations • 14 Feb 2024 • Won-Seok Choi, Hyundo Lee, Dong-Sig Han, Junseok Park, Heeyeon Koo, Byoung-Tak Zhang
Recent machine learning algorithms have been developed using well-curated datasets, which often require substantial cost and resources.
no code implementations • 23 May 2023 • Kibeom Kim, Hyundo Lee, Min Whoo Lee, Moonheon Lee, Minsu Lee, Byoung-Tak Zhang
Tasks that involve interaction with various targets are called multi-target tasks.
no code implementations • CVPR 2023 • Hyundo Lee, Inwoo Hwang, Hyunsung Go, Won-Seok Choi, Kibeom Kim, Byoung-Tak Zhang
Our method, coined Learning by Sketching (LBS), learns to convert an image into a set of colored strokes that explicitly incorporate the geometric information of the scene in a single inference step without requiring a sketch dataset.
no code implementations • 31 Oct 2022 • Won-Seok Choi, Dong-Sig Han, Hyundo Lee, Junseok Park, Byoung-Tak Zhang
In Self-Supervised Learning (SSL), it is known that frequent occurrences of the collision in which target data and its negative samples share the same class can decrease performance.
no code implementations • 20 Oct 2022 • Dong-Sig Han, Hyunseo Kim, Hyundo Lee, Je-Hwan Ryu, Byoung-Tak Zhang
Recently, adversarial imitation learning has shown a scalable reward acquisition method for inverse reinforcement learning (IRL) problems.
no code implementations • 1 Jan 2021 • Jiseob Kim, Seungjae Jung, Hyundo Lee, Byoung-Tak Zhang
One of the difficulties in modeling real-world data is their complex multi-manifold structure due to discrete features.
no code implementations • 1 Jan 2021 • Dong-Sig Han, Hyunseo Kim, Hyundo Lee, Je-Hwan Ryu, Byoung-Tak Zhang
The formulation draws a strong connection between adversarial learning and energy-based reinforcement learning; thus, the architecture is capable of recovering a reward function that induces a multi-modal policy.
no code implementations • 2 Dec 2020 • Taehyeong Kim, Injune Hwang, Hyundo Lee, Hyunseo Kim, Won-Seok Choi, Joseph J. Lim, Byoung-Tak Zhang
Active learning is widely used to reduce labeling effort and training time by repeatedly querying only the most beneficial samples from unlabeled data.
no code implementations • 25 Sep 2019 • Jiseob Kim, Seungjae Jung, Hyundo Lee, Byoung-Tak Zhang
We present a generative adversarial network (GAN) that conducts manifold learning and alignment (MLA): A task to learn the multi-manifold structure underlying data and to align those manifolds without any correspondence information.
no code implementations • 3 Jun 2019 • Jiseob Kim, Seungjae Jung, Hyundo Lee, Byoung-Tak Zhang
We present an encoder-powered generative adversarial network (EncGAN) that is able to learn both the multi-manifold structure and the abstract features of data.