| 발표자 | 양희윤 |
|---|---|
| 발표일자 | 2023-03-22 |
| 저자 | Shuqing Bian, Wayne Xin Zhao, Jinpeng Wang, and Ji-Rong Wen |
| 학회명 | CIKM 2022 |
| 논문지 |
댓글 1
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jinsuby
2023.03.22 19:13
User의 Embedding vector 정보를 잘 표현하기 위해 Attention 기반의 Representation, Mixup 기반의 Representation을 동시에 활용하는 논문임. 이렇게 표현된 Representation으로 같은 user의 augmented representation 끼리 뭉치게하는 InfoNCE Loss와, user representation과 paired item representation 끼리 뭉치게 하는 InfoNCE Loss로 학습함. 다만, 두 Augmentation간의 기대효과가 어떤 차별점을 보이는지는 조금 더 이해해보아야 함. Mixup의 기대효과에 대해서 고민을 해본다면.. 다른 해석이 이뤄질 수도 있을 것 같음.
2023
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2023.09.13
발표자: 이재준
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발표자: 안지민
발표일자: 2023-09-13
저자: Yue Wang, Hung Le., et al
학회명: arXiv 2023
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발표일자: 2023-09-06
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발표일자: 2023-09-06
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Do We Really Need a Learnable Classifier at the End of Deep Neural Network?
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발표일자: 2023-09-06
학회명: NeurIPS 2022
SCOTT: Self-Consistent Chain-of-Thought Distillation
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발표일자: 2023-08-23
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학회명: ACL 2023
DualCoop: Fast Adaptation to Multi-Label Recognition with Limited Annotations
2023.08.23
발표자: 송경렬
발표일자: 2023-08-23
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학회명: Neurlips 2022
논문지: Neurlips 2022
CLIP is Also an Efficient Segmenter: A Text-Driven Approach for Weakly-Supervised Semantic Segmentation
2023.08.23
발표자: 최준수
발표일자: 2023-08-23
저자: Yuqi Lin, Minghao Chen et al.
학회명: CVPR 2023
논문지: CVPR 2023
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2023.08.09
발표자: 김도영
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Over-Training with Mixup May Hurt Generalization
2023.08.09
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학회명: ICLR 2023
Style Normalization and Restitution for Domain Generalization and Adaptation
2023.08.01
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학회명: CVPR 2020
SSR_An Efficient and Robust Framework for Learning with Unknown Label Noise
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발표자: 이진섭
발표일자: 2023-08-02
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학회명: BMVC 2022
Tackling Data Heterogeneity in Federated Learning with Class Prototypes
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발표일자: 2023-08-02
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학회명: AAAI 2023