2021 Seminars
No. |
Speaker |
Title |
Reference |
Slides |
Date |
|---|---|---|---|---|---|
1 |
Mehret Abraha |
Edge detection-based boundary box construction algorithm for improving |
S. T. Blue, M. Brindha. Edge detection based boundary box construction algorithm for improving the precision of object detection in YOLOv3 |
Jan, 2021 |
|
2 |
Sanghyuck Na |
MirrorGAN: Learning Text-to-image Generation by Redescription |
T. Qiao, J. Zhang, D. Xu, D. Tao. MirrorGAN: Learning Text-to-image Generation by Redescription |
Jan, 2021 |
|
3 |
Sung-eun Jang |
Few-shot link prediction via graph neural networks for covid-19 drug-repurposing |
V. N. Ioannidis, D. Zheng, G. Karypis. Few-shot link prediction via graph neural networks for Covid-19 drug-repurposing |
Feb, 2021 |
|
4 |
Anh Tran |
Combining Deep Reinforcement Learning and Search for Imperfect-Information Games |
N. Brown, A. Bakhtin, A. Lerer, Q. Gong. Combining Deep Reinforcement Learning and Search for Imperfect-Information Games |
Feb, 2021 |
|
5 |
Park MinKyu |
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence |
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence |
March, 2021 |
|
6 |
Mehret Abraha |
Adversarial Network for edge detection |
Z. Zeng, Y. Yu, K. Wong. Adversarial Network for edge detection |
March, 2021 |
|
7 |
Sanghyuck Na |
Tensor Fusion Network for Multimodal Sentiment Analysis |
A. Zadeh, M. Chen et. al., Tensor Fusion Network for Multimodal Sentiment Analysis |
April, 2021 |
|
8 |
Chang-Hoon Jeong |
Deep Meta Learning Learning-to-Learn in Neural Networks |
[Multiple Refrences] |
April, 2021 |
|
9 |
Sung-eun Jang |
Protein structure Prediction |
[Multiple Refrences] |
May, 2021 |
|
10 |
Anh Tran |
Video Summarization Using Deep Neural Networks |
E. Apostolids, E. Adamantidou et al. Video Summarization Using Deep Neural Networks |
May, 2021 |
|
11 |
Sanghyun Seo |
Meta-Information Guided Meta-Learning for Few-Shot Relation Classification |
B. Dong, Y. Yao, R. Xie. Meta-Information Guided Meta-Learning for Few-Shot Relation Classification |
May, 2021 |
|
12 |
Ershang Tian |
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection |
S. Beery, G. Wu et al. Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection |
May, 2021 |
|
13 |
Hiskias Dingeto |
Eye In-Painting with Exemplar Generative Adversarial Networks |
B, Dolhansky, C. Ferrer. Eye In-Painting with Exemplar Generative Adversarial Networks |
June, 2021 |
|
14 |
Seoyeong Lee |
A CNNLST model for gold price time-series forecasting |
I. Livieris, E. Pintelas, P. Pintelas. A CNN–LSTM model for gold price time-series forecasting |
June, 2021 |
|
15 |
Beomsu Park |
Deep Leakage from gradients |
L. Zhu, Z. Liu, S. Han. Deep Leakage from Gradients |
June, 2021 |
|
16 |
Mehret Abraha |
Meta-Learning to Detect Rare Objects |
Y. Wang, D. Ramanan, M. Hebert. Meta-Learning to Detect Rare Objects |
July, 2021 |
|
17 |
Hiskias Dingeto |
Targeted Speech Adversarial Example Generation With Generative Adversarial Network |
D. Wang et al. Targeted Speech Adversarial Example Generation With Generative Adversarial Network |
July, 2021 |
|
18 |
Park MinKyu |
MixBoost: Synthetic Oversampling using Boosted Mixup for Handling Extreme Imbalance |
A. Kabea et al. MixBoost: Synthetic Oversampling with Boosted Mixup for Handling Extreme Imbalance |
Aug, 2021 |
|
19 |
Anh Tran |
Unsupervised Video Summarization with Adversarial LSTM Networks |
B. Mahasseni et al. Unsupervised Video Summarization with Adversarial LSTM Networks |
Aug, 2021 |
|
20 |
Ershang Tian |
Meta-RCNN: Meta-Learning for Few-Shot Object Detection |
X. Wu et al. Meta-RCNN: Meta-Learning for Few-Shot Object Detection |
Aug, 2021 |
|
21 |
Seoyeong Lee |
Playing Atari with Deep Reinforcement Learning |
V. Mnih et al. Playing Atari with Deep Reinforcement Learning |
Sept, 2021 |
|
22 |
Sujeong Chae |
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks |
C. Finn et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks |
Sept, 2021 |
|
23 |
Hiskias Dingeto |
Ensemble Adversarial Training: Attacks and Defenses |
F. Tramer et al. Ensemble Adversarial Training: Attacks and Defenses |
Sept, 2021 |
|
24 |
Mehret Abraha |
Siamese Neural Networks for One-shot Image Recognition |
G. Koch et al. Siamese Neural Networks for One-shot Image Recognition |
Sept, 2021 |
|
25 |
Ershang Tian |
Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning |
X. Yan et al. Meta R-CNN : Towards General Solver for Instance-level Few-shot Learning |
Sept, 2021 |
|
26 |
Seoyeong Lee |
Deep Recurrent Q-Learning for Partially Observable MDPs |
M. Hausknecht et al. Deep Recurrent Q-Learning for Partially Observable MDPs |
Dec, 2021 |
|
27 |
Sujeong Chae |
Siamese Neural Networks for One-shot Image Recognition |
G. Koch et al. Siamese Neural Networks for One-shot Image Recognition |
Dec, 2021 |
2020 Seminars
No. |
Speaker |
Title |
Reference |
Slides |
Date |
|---|---|---|---|---|---|
1 |
Hyojin Ki |
Attention Mechanism |
D. Bahdanau, K. Cho, Y. Bengio. Neural Machine Translation by Jointly Learning to Align and Translate |
Jan, 2020 |
|
2 |
Jonghyun Ko |
Densely Connected Convolutional Networks (DenseNet) |
G. Huang, Z. Liu, L. Maaten. Densely Connected Convolutional Networks |
Jan, 2020 |
|
3 |
Sanghyun Seo |
Meta Learning for Low-Resource Natural Language Understanding |
Z. Dou et al. Investigating Meta-Learning Algorithms for Low-Resource Natural Language Understanding Tasks, C. Finn et al. Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks |
Feb, 2020 |
|
4 |
Park MinKyu |
(Learning to) Learn from Unlabeled Data |
Yalniz, I. Zeki, et al. Billion-scale semi-supervised learning for image classification |
Feb, 2020 |
|
5 |
Changhoon Jeong |
Meta Reinforcement Learning as Task Inference |
J. Humplik, A. Galashov, L. Hasenclever, P. A. Ortega, Y. Teh, N. Heess. Meta Reinforcement Learning As Task Inference |
Apr, 2020 |
|
6 |
Sung-eun Jang |
Graph Neural Networks |
Kipf, T. N., & Welling, M. (2016). Semi-supervised classification with graph convolutional networks, Hamilton, W., Ying, Z., & Leskovec, J. (2017). Inductive representation learning on large graphs, Veličković, P., Cucurull, G., Casanova, A., Romero, A., Lio, P., & Bengio, Y. Graph attention networks |
Apr, 2020 |
|
7 |
Sanghyuck Na |
StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks |
H. Zhang, T. Xu, H. Li, S. Zhang, X. Wang, X. Huang, D. Metaxas. StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks |
Apr, 2020 |
|
8 |
Daeung Kim |
Transfer Learning in NLP |
A. Chronopoulou, C. Baziotis, A. Potamianos. An Embarrassingly Simple Approach for Transfer Learning from Pretrained Language Models |
Apr, 2020 |
|
9 |
Sanghyuck Na |
Perceptual Losses for Real-Time Style Transfer and Super-Resolution |
J. Johnson, A. Alahi, Li Fei-Fei. Perceptual Losses for Real-Time Style Transfer and Super-Resolution |
May, 2020 |
|
10 |
Daeung Kim |
Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks |
T. Bansal, R. Jha, A. McCallum. Learning to Few-Shot Learn Across Diverse Natural Language Classification Tasks |
June, 2020 |
|
11 |
Chea Navy |
Transfer Learning & Domain Adaptation |
S. J. Pan, Q. Y. Fellow. A Survey on Transfer Learning |
July, 2020 |
|
12 |
Park MinKyu |
MixMatch: A Holistic Approach to Semi-Supervised Learning |
D. Berthelot, N. Carlini, I. Goodfellow, A. Oliver, N. Papernot, C. Raffel. MixMatch: A Holistic Approach to Semi-Supervised Learning |
July, 2020 |
|
13 |
Yeonji Lim |
Playing Atari with Deep Reinforcement Learning |
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, M. Riedmiller. Playing Atari with Deep Reinforcement Learning |
July, 2020 |
|
14 |
Mehret Abraha |
You Only Look Once: Unified, Read Time Object Detection |
J. Redmon, S. Divvala, R. Girshick, A. Farhad. You Only Look Once: Unified, Real-Time Object Detection |
July, 2020 |
|
15 |
Sung-eun Jang |
Edge-Labeling Graph Neural Network for Few-shot Learning |
J. Kim, T. Kim, S. Kim, C. D. Yoo. Edge-Labeling Graph Neural Network for Few-shot Learning |
Aug, 2020 |
|
16 |
Shin SeungYun |
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition |
GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition |
Aug, 2020 |
|
17 |
Park MinKyu |
Modern Data Augmentation Techniques |
H. Zhang, M. Cisse, Y. N Dauphin, D. Lopez-Paz, “Mixup: Beyond empirical risk minimization,”, S. Yun, D. Han, S. Joon Oh, S. Chun, J. Choe, Y. Yoo, “Cutmix: Regularization strategy to train strong classifiers with localizable features,” |
Sept, 2020 |
|
18 |
Daeung Kim |
Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering |
M. Yan, H. Zhang, D. Jin, J. T. Zhou. Multi-source Meta Transfer for Low Resource Multiple-Choice Question Answering |
Sept, 2020 |
|
19 |
Anh Tran |
Texas Hold’Em and Reinforcement Learning |
X. C. Zhang, Y. Li. A Texas Hold’Em decision model based on Reinforcement Learning |
Sept, 2020 |
|
20 |
Luna Jang |
A Multiple Object Tracking Algorithm Based on YOLO Detection |
L. Tan; X. Dong; Y. Ma, C. Yu. A Multiple Object Tracking Algorithm Based on YOLO Detection |
Oct, 2020 |
|
21 |
Sung-eun Jang |
Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting |
D. Liu, D. Zhang, Y. Song, F. Zhang, L. O'Donnell, H. Huang, M. Chen, W. Cai. Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting |
Nov, 2020 |
|
22 |
Sanghyuck Na |
Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks |
J. Zhu, T. Park, P. Isola, A. A. Efros. Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks |
Nov, 2020 |
2019 Seminars
No. |
Speaker |
Title |
Reference |
Slides |
Date |
|---|---|---|---|---|---|
1 |
Sanghyun Seo |
Deep Metric Learning with Hierarchical Triplet Loss |
Ge, Weifeng, et al. "Deep metric learning with hierarchical triplet loss." Proceedings of the European Conference on Computer Vision (ECCV). 2018 |
Jan, 2019 |
|
2 |
Park MinKyu |
Zero-Shot Learning for Computer Vision |
Zero-Shot Learning for Computer Vision, CVPR 2017 |
Jan, 2019 |
|
3 |
Hyojin Ki |
Learning Deep Structure-Preserving Image-Text Embeddings |
Wang, Liwei, Yin Li, and Svetlana Lazebnik. "Learning deep structure-preserving image-text embeddings." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016 |
Feb, 2019 |
|
4 |
Seongeun Jang |
MolGAN: An implicit generative model for small molecular graphs |
N. De Cao and T. Kipf. Molgan: An implicit generative model for small molecular graphs. arXiv:1805.11973, 2018. |
Mar, 2019 |
|
5 |
Sunwoo Kim |
Iris recognition Towards More Accurate Iris Recognition Using Deeply Learned Spatially Corresponding Features |
Z. Zhao and A. Kumar, "Towards More Accurate Iris Recognition Using Deeply Learned Spatially Corresponding Features," 2017 IEEE International Conference on Computer Vision (ICCV), 2017, pp. 3829-3838, doi: 10.1109/ICCV.2017.411. |
Mar, 2019 |
|
6 |
Daeung Kim |
Convolutional Neural Networks for Sentence Classification |
Y. Kim, Convolutional Neural Networks for Sentence Classification |
Apr, 2019 |
|
7 |
Sanghyuck Na |
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks |
A. Radford, L. Metz, S. Chintala. Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks |
Apr, 2019 |
|
8 |
Changhoon Jeong |
Trust Region Policy Optimization |
J. Schulman, S. Levine, P. Moritz, M. I. Jordan, P. Abbeel. Trust Region Policy Optimization |
May, 2019 |
|
9 |
Sanghyun Seo |
Networks Dissection & GAN Dissection |
D. Bau, Jun-Yan Zhu, H. Strobelt, B. Zhou, J. B. Tenenbaum, W. T. Freeman, A. Torralba. GAN Dissection: Visualizing and Understanding Generative Adversarial Networks |
May, 2019 |
|
10 |
Park MinKyu |
Learning Deep Representations of Fine-Grained Visual Descriptions |
S. Reed, Z. Akata, H. Lee, B. Schiele. Learning Deep Representations of Fine-Grained Visual Descriptions |
June, 2019 |
|
11 |
Hyojin Ki |
Show, Attend and Tell: Neural Image Caption Generation with visual Attention |
Xu, Kelvin, et al. "Show, attend and tell: Neural image caption generation with visual attention." International conference on machine learning. 2015. (ICML 2015) |
June, 2019 |
|
12 |
Changhoon Jeong |
Playing hard exploration games by watching YouTube |
Y. Aytar, T. Pfaff, D. Budden, T. Le Paine, Z. Wang, N. de Freitas. Playing hard exploration games by watching YouTube |
July, 2019 |
|
13 |
Jang SeongEun |
Medical Image Segmentation with U-Net |
O. Ronneberger, P. Fischer, T. Brox. U-Net: Convolutional Networks for Biomedical Image Segmentation |
July, 2019 |
|
14 |
Sunwoo Kim |
Fully-Convolutional Siamese Networks for Object Tracking |
L. Bertinetto, J. Valmadre, J. F. Henriques, A. Vedaldi, P. H. S. Torr. Fully-Convolutional Siamese Networks for Object Tracking |
July, 2019 |
|
15 |
Daeung Kim |
Character-level Convolutional Networks for Text Classification |
X. Zhang, J. Zhao, Y. LeCun. Character-level Convolutional Networks for Text Classification |
Aug, 2019 |
|
16 |
Sanghyuck Na |
Semantic Image synthesis with Spatially-Adaptive Normalization |
T. Park, M. Liu, T. Wang, J. Zhu. Semantic Image Synthesis with Spatially-Adaptive Normalization |
Aug, 2019 |
|
17 |
Hyojin Ki |
Knowing when to look: Adaptive Attention via A Visual Sentinel for Image Captioning |
Jiasen Lu, Caiming Xiong, Devi Parikh, Richard Socher. Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning |
Aug, 2019 |
|
18 |
Daeung Kim |
DeViSE: A Deep Visual-Semantic Embedding Model |
A. Frome, G. S. Corrado, J. Shlens, S. Bengio, J. Dean, M. Ranzato, T. Mikolov. DeViSE: A Deep Visual-Semantic Embedding Model |
Sept, 2019 |
|
19 |
Changhoon Jeong |
Meta Learning |
O. Vinyals et al. Matching Networks for One Shot Learning, Z. Li et al. Meta-SGD: Learning to Learn Quickly for Few-Shot Learning, A. Nichol et al. On First-Order Meta-Learning Algorithms |
Sept, 2019 |
|
20 |
Sanghyuck Na |
FIGR : Few-shot Image Generation with Reptile |
L. Clouâtre, M. Demers. FIGR: Few-shot Image Generation with Reptile |
Sept, 2019 |
|
21 |
Sunwoo Kim |
General semantic Image segmentation & PSAnet for few shot semantic segmentation |
K. Wang, J. Hao Liew, Y. Zou, D. Zhou, J. Feng. PANet: Few-Shot Image Semantic Segmentation with Prototype Alignment |
Oct, 2019 |
|
22 |
Sanghyun Seo |
Recent Language Models |
Vaswani, Ashish, et al. "Attention is all you need." Advances in neural information processing systems. 2017. |
Oct, 2019 |
|
23 |
Park MinKyu |
Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-grained Classification |
L. Niu, A. Veeraraghavan, A. Sabharwal. Webly Supervised Learning Meets Zero-shot Learning: A Hybrid Approach for Fine-grained Classification |
Nov, 2019 |
|
24 |
Jang SeongEun |
Drug Discovery and Development |
Jaeger, Sabrina, Simone Fulle, and Samo Turk. "Mol2vec: unsupervised machine learning approach with chemical intuition." Journal of chemical information and modeling 58.1 (2018): 27-35. |
Nov, 2019 |
|
25 |
Mehret Abraha |
Artificial intellegince and Robot Path Planning |
X. Dai, S. Long, Z. Zhang, D. Gong. Mobile Robot Path Planning Based on Ant Colony Algorithm With A* Heuristic Method |
Nov, 2019 |
|
26 |
Changhoon Jeong |
Generative Model |
I. J. Goodfellow et al. Generative Adversarial Nets, D. P. Kingma et al. Auto-Encoding Variational Bayes |
Dec, 2019 |
2018 Seminars
No. |
Speaker |
Title |
Reference |
Slides |
Date |
|---|---|---|---|---|---|
1 |
Changhoon Jeong |
Neural Architecture Search |
B. Zoph and Q. V. Le, “Neural architecture search with reinforcement learning,” 2016, arXiv:1611.01578. |
April, 2018 |
|
2 |
Sanghyun Seo |
Learning Deep Features for Discriminative Localization |
Lin, Min, Qiang Chen, and Shuicheng Yan. "Network in network." arXiv preprint arXiv:1312.4400 (2013) |
April, 2018 |
|
3 |
Hyun-Min Park |
Pattern Recognition : Bayesian Classifier |
Pattern Recognition and Machine Learning, Christopher M. Bishop |
May, 2018 |
|
4 |
Hyun-Min Park |
See, Hear, and Read: Deep Aligned Representations |
Aytar Y, Vondrick C, Torralba A (2017) See, hear, and read: deep aligned representations. arXiv preprint arXiv:1706.00932 |
Nov, 2018 |
2017 Seminars
No. |
Speaker |
Title |
Reference |
Slides |
Date |
|---|---|---|---|---|---|
1 |
Sanghyun Seo |
Playing Atari With Deep Reinforcement Learning |
Mnih, Volodymyr, et al. “Playing atari with deep reinforcement learning” arXiv preprint arXiv:1312.5602 (2013). |
2017-03-10 |
|
2 |
Jisang Yun |
Comparison of word2vec Models Focus on the semantic Segmentation |
신수미, 배수영. “의미구분 단위에 따른 word2vec 모델 비교” 한국정보과학회 학술발표논문집,(2016): 753-755. |
2017-03-17 |
|
3 |
Hoonyeob Na |
Deep Learning for stock prediction using numerical and textual information |
Ryo Akita, Akira Yoshihara, Takashi Matsubara, Kuniaki Uehara “Deep Learning for stock prediction using numerical and textual information” ICIS 2016, June26-20, 2016, Okayama, Japan , 26-29ek |
2017-03-31 |
|
4 |
Arjun Magotra |
Learning CNN-LSTM Architectures for Image Caption Generation |
Soh, Moses. “Learning CNN-LSTM Architectures for Image Caption Generation.“ |
2017-04-07 |