1 citations · 2 across the 4 of their papers we have counts for
4 papers
Unsupervised Accuracy Estimation of Deep Visual Models using Domain-Adaptive Adversarial Perturbation without Source Samples
JoonHo Lee, Jae Oh Woo, Hankyu Moon +1
Deploying deep visual models can lead to performance drops due to the discrepancies between source and target distributions. Several approaches leverage labeled source data to esti…
Self-Supervised Contrastive Representation Learning for 3D Mesh Segmentation
Ayaan Haque, Hankyu Moon, Heng Hao +3
3D deep learning is a growing field of interest due to the vast amount of information stored in 3D formats. Triangular meshes are an efficient representation for irregular, non-uni…
PatchNet: Unsupervised Object Discovery based on Patch Embedding
Hankyu Moon, Heng Hao, Sima Didari +2
We demonstrate that frequently appearing objects can be discovered by training randomly sampled patches from a small number of images (100 to 200) by self-supervision. Key to this…
Highly Efficient Representation and Active Learning Framework and Its Application to Imbalanced Medical Image Classification
Heng Hao, Hankyu Moon, Sima Didari +2
We propose a highly data-efficient active learning framework for image classification. Our novel framework combines: (1) unsupervised representation learning of a Convolutional Neu…