전체 글(24)
-
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training
ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training Eldele, E., Ragab, M., Chen, Z., Wu, M., Kwoh, C. K., Li, X., & Guan, C. (2021). ADAST: Attentive Cross-domain EEG-based Sleep Staging Framework with Iterative Self-Training. arXiv preprint arXiv:2107.04470 1. Domain Adaptation이란 무엇인가?? Domain Adaptation에서 알아야되는 것 1. Source Domain : 학습에 활용되는 데이터셋 분포 도메인 ..
2022.04.25 -
[논문읽기] A Simple Framework for Contrastive Learning of Visual Representations(SimCLR)
A Simple Framework for Contrastive Learning of Visual Representations Chen, T., Kornblith, S., Norouzi, M., & Hinton, G. (2020, November). A simple framework for contrastive learning of visual representations. In International conference on machine learning (pp. 1597-1607). PMLR. Main idea - 본 논문 SimCLR에서 기존 unsupervised contrastive learning 방법들과 가장 큰 차이점은 memory bank를 활용하지 않는 점이 존재한다. - Memory ..
2022.03.04 -
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification
Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification Wang, P., Han, K., Wei, X. S., Zhang, L., & Wang, L. (2021). Contrastive Learning based Hybrid Networks for Long-Tailed Image Classification. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 943-952). Data-imbalanced Problem 데이터 불균형 즉, 본 논문의 제목에 있는 "Long-tailed" 환경을 위 그림을 통해 알..
2021.12.21 -
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distilltation
Be Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distilltation ####Zhang, L., Song, J., Gao, A., Chen, J., Bao, C., & Ma, K. (2019). Be your own teacher: Improve the performance of convolutional neural networks via self distillation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 3713-3722). Abstract 본 논문에서는 self-distillation..
2021.06.02 -
[논문정리] Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images
Collaborative Learning of Semi-Supervised Segmentation and Classification for Medical Images Zhou, Y., He, X., Huang, L., Liu, L., Zhu, F., Cui, S., & Shao, L. (2019). Collaborative learning of semi-supervised segmentation and classification for medical images. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 2079-2088). Semi Supervised Learning Unsupervi..
2021.05.17 -
M2m : Imbalanced Classification via Major-to-minor Translation
M2m : Imbalanced Classification via Major-to-minor Translation Kim, J., Jeong, J., & Shin, J. (2020). M2m: Imbalanced classification via major-to-minor translation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 13896-13905). Abstract 실제 환경에서의 정답이 있는 학습 데이터셋은 일반적으로 굉장히 불균형적이지만, 실제 해당 데이터를 학습하는 모델을 균형적인 테스트 평가 기준을 만족해야 된다. 해당 논문에서는, 많이 등장하는(more-frequent..
2021.05.02