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Generalization to unseen data remains poorly understood for deep learning classification and foundation models, especially in the open set scenario.
Comparing community structure identification
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Very deep convolutional networks for large-scale image recognition
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A unified perspective on multi-domain and multi-task learning
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Predicting deep zero-shot convolutional neural networks using textual descriptions
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
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Learning deep representations of fine-grained visual descriptions
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Understanding intermediate layers using linear classifier probes
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Unsupervised pixel-level domain adaptation with generative adversarial networks
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Sharp minima can generalize for deep nets
L. Dinh, R. Pascanu, S. Bengio, and Y. Bengio · 2017
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On large-batch training for deep learning: Generalization gap and sharp minima
N. S. Keskar, D. Mudigere, J. Nocedal, M. Smelyanskiy, and P. T. P. Tang · 2017
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Adaptive deep learning through visual domain localization
G. Angeletti, B. Caputo, and T. Tommasi · 2018
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Learning sparse neural networks through l 0 l_{0} regularization
C. Louizos, M. Welling, and D. P. Kingma · 2018
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On first-order meta-learning algorithms
A. Nichol, J. Achiam, and J. Schulman · 2018
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Deep visual domain adaptation: A survey
M. Wang and W. Deng · 2018
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Zero-shot learning – a comprehensive evaluation of the good, the bad and the ugly
Chinese calligraphy styles by calligraphers
Y. Wang · 2020
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Contrastive embedding for generalized zero-shot learning
Z. Han, Z. Fu, S. Chen, and J. Yang · 2021
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Iterative label cleaning for transductive and semi-supervised few-shot learning
M. Lazarou, T. Stathaki, and Y. Avrithis · 2021
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Pyramid vision transformer: A versatile backbone for dense prediction without convolutions
W. Wang, E. Xie, X. Li, D.-P. Fan, K. Song, D. Liang, T. Lu, P. Luo, and L. Shao · 2021
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Finetuned language models are zero-shot learners
J. Wei, M. Bosma, V. Y. Zhao, K. Guu, A. W. Yu, B. Lester, N. Du, A. M. Dai, and Q. V. Le · 2021
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Cvt: Introducing convolutions to vision transformers
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Y. Xian, C. H. Lampert, B. Schiele, and Z. Akata · 2018
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Tackling partial domain adaptation with self-supervision
S. Bucci, A. D’Innocente, and T. Tommasi · 2019
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A closer look at few-shot classification
W.-Y. Chen, Y.-C. Liu, Z. Kira, Y.-C. F. Wang, and J.-B. Huang · 2019
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A baseline for few-shot image classification
G. S. Dhillon, P. Chaudhari, A. Ravichandran, and S. Soatto · 2019
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Learning to discover novel visual categories via deep transfer clustering
K. Han, A. Vedaldi, and A. Zisserman · 2019
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Contrastive adaptation network for unsupervised domain adaptation
G. Kang, L. Jiang, Y. Yang, and A. G. Hauptmann · 2019
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Language models are few-shot learners
T. Brown, B. Mann, N. Ryder, M. Subbiah, J. D. Kaplan, P. Dhariwal, A. Neelakantan, P. Shyam, G. Sastry, A. Askell, et al · 2020
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H. Wu, B. Xiao, N. Codella, M. Liu, X. Dai, L. Yuan, and L. Zhang · 2021
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Generalized out-of-distribution detection: A survey
J. Yang, K. Zhou, Y. Li, and Z. Liu · 2021
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Understanding deep learning (still) requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2021
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Large language models are zero-shot reasoners
T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa · 2022
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A review of generalized zero-shot learning methods
F. Pourpanah, M. Abdar, Y. Luo, X. Zhou, R. Wang, C. P. Lim, X.-Z. Wang, and Q. J. Wu · 2022
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Generalized category discovery
S. Vaze, K. Han, A. Vedaldi, and A. Zisserman · 2022
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Metaformer is actually what you need for vision
W. Yu, M. Luo, P. Zhou, C. Si, Y. Zhou, X. Wang, J. Feng, and S. Yan · 2022
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ConvNeXt V2: Co-designing and scaling convnets with masked autoencoders
S. Woo, S. Debnath, R. Hu, X. Chen, Z. Liu, I. S. Kweon, and S. Xie · 2023
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No representation rules them all in category discovery
S. Vaze, A. Vedaldi, and A. Zisserman · 2024
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