Fetching the paper…
Reading the bibliography…
With the ever-increasing complexity of large-scale pre-trained models coupled with a shortage of labeled data for downstream training, transfer learning has become the primary approach in many fields, including natural language processing, computer vision, and multi-modal learning.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in
2008
Earlier work this paper cites.
A. Krizhevsky, G. Hinton,
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in
2009
Earlier work this paper cites.
J. Xiao, J. Hays, K. A. Ehinger, A. Oliva, and A. Torralba, “Sun database: Large-scale scene recognition from abbey to zoo,” in
2010
Earlier work this paper cites.
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg,
2011
Earlier work this paper cites.
O. M. Parkhi, A. Vedaldi, A. Zisserman, and C. V. Jawahar, “Cats and dogs,” in
2012
Earlier work this paper cites.
2013
Earlier work this paper cites.
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi, “Describing textures in the wild,” in
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Earlier work this paper cites.
J. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,”
2016
Earlier work this paper cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in
2017
Earlier work this paper cites.
C. Sun, A. Shrivastava, S. Singh, and A. K. Gupta, “Revisiting unreasonable effectiveness of data in deep learning era,”
2017
Earlier work this paper cites.
S.-A. Rebuffi, H. Bilen, and A. Vedaldi, “Learning multiple visual domains with residual adapters,” in
2017
Earlier work this paper cites.
I. Loshchilov and F. Hutter, “SGDR: Stochastic gradient descent with warm restarts,” in
2017
Earlier work this paper cites.
N. Mishra, M. Rohaninejad, X. Chen, and P. Abbeel, “A simple neural attentive meta-learner,”
2017
Earlier work this paper cites.
M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L.-C. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,”
2018
Earlier work this paper cites.
A. Madry, A. Makelov, L. Schmidt, D. Tsipras, and A. Vladu, “Towards deep learning models resistant to adversarial attacks,” in
2018
Earlier work this paper cites.
B. Oreshkin, P. Rodríguez López, and A. Lacoste, “Tadam: Task dependent adaptive metric for improved few-shot learning,”
2018
Earlier work this paper cites.
2019
Earlier work this paper cites.
V. Cheplygina, M. De Bruijne, and J. P. Pluim, “Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis,”
2019
Earlier work this paper cites.
S. Kornblith, J. Shlens, and Q. V. Le, “Do better imagenet models transfer better?,” in
2019
Earlier work this paper cites.
N. Houlsby, A. Giurgiu, S. Jastrzebski, B. Morrone, Q. de Laroussilhe, A. Gesmundo, M. Attariyan, and S. Gelly, “Parameter-efficient transfer learning for nlp,” in
2019
Earlier work this paper cites.
J. Hu, L. Shen, S. Albanie, G. Sun, and E. Wu, “Squeeze-and-excitation networks,” 2019
2019
Earlier work this paper cites.
K. Lee, S. Maji, A. Ravichandran, and S. Soatto, “Meta-learning with differentiable convex optimization,” in
2019
Earlier work this paper cites.
F. Ramzan, M. U. G. Khan, A. Rehmat, S. Iqbal, T. Saba, A. Rehman, and Z. Mehmood, “A deep learning approach for automated diagnosis and multi-class classification of alzheimer’s disease stages using resting-state fmri and residual neural networks,”
2019
Earlier work this paper cites.
2019
Earlier work this paper cites.
F. Zhuang, Z. Qi, K. Duan, D. Xi, Y. Zhu, H. Zhu, H. Xiong, and Q. He, “A comprehensive survey on transfer learning,”
2020
Earlier work this paper cites.
V. Papyan, X. Han, and D. L. Donoho, “Prevalence of neural collapse during the terminal phase of deep learning training,”
2020
Earlier work this paper cites.
Y. Tian, Y. Wang, D. Krishnan, J. B. Tenenbaum, and P. Isola, “Rethinking few-shot image classification: a good embedding is all you need?,” in
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
2020
Cited alongside, same era.
V. Papyan, “Traces of class/cross-class structure pervade deep learning spectra,”
2020
Cited alongside, same era.
D. G. Mixon, H. Parshall, and J. Pi, “Neural collapse with unconstrained features,”
2020
Cited alongside, same era.
2020
Cited alongside, same era.
S. Liu, J. Niles-Weed, N. Razavian, and C. Fernandez-Granda, “Early-learning regularization prevents memorization of noisy labels,”
2020
P. Wang, H. Liu, C. Yaras, L. Balzano, and Q. Qu, “Linear convergence analysis of neural collapse with unconstrained features,” in
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
2022
Closest in time.
T. Behnia, G. R. Kini, V. Vakilian, and C. Thrampoulidis, “On the implicit geometry of cross-entropy parameterizations for label-imbalanced data,” in
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Cited alongside, same era.
T. Wang and P. Isola, “Understanding contrastive representation learning through alignment and uniformity on the hypersphere,” in
2020
Cited alongside, same era.
S. Kornblith, T. Chen, H. Lee, and M. Norouzi, “Why do better loss functions lead to less transferable features?,” in
2021
Cited alongside, same era.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” in
2021
Cited alongside, same era.
2021
Cited alongside, same era.
X. Chen and K. He, “Exploring simple siamese representation learning,”
2021
Cited alongside, same era.
Z. Deng, L. Zhang, K. Vodrahalli, K. Kawaguchi, and J. Zou, “Adversarial training helps transfer learning via better representations,” in
2021
Cited alongside, same era.
C. Fang, H. He, Q. Long, and W. J. Su, “Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training,”
2021
Cited alongside, same era.
2022
Closest in time.
2022
Closest in time.
H. He and W. J. Su, “A law of data separation in deep learning,”
2022
Closest in time.
2022
Closest in time.
T. Galanti, “A note on the implicit bias towards minimal depth of deep neural networks,”
2022
Closest in time.
M. Chen, D. Y. Fu, A. Narayan, M. Zhang, Z. Song, K. Fatahalian, and C. Ré, “Perfectly balanced: Improving transfer and robustness of supervised contrastive learning,” in
2022
Closest in time.
2022
Closest in time.
2023
Closest in time.
OpenAI, “Gpt-4 technical report,”
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
V. Kothapalli, “Neural collapse: A review on modelling principles and generalization,”
2023
Closest in time.
2023
Closest in time.
Z. Zhong, J. Cui, Y. Yang, X. Wu, X. Qi, X. Zhang, and J. Jia, “Understanding imbalanced semantic segmentation through neural collapse,” in
2023
Closest in time.
2023
Closest in time.
T. Behnia, G. R. Kini, V. Vakilian, and C. Thrampoulidis, “On the implicit geometry of cross-entropy parameterizations for label-imbalanced data,” in
2023
Closest in time.
W. Liu, L. Yu, A. Weller, and B. Schölkopf, “Generalizing and decoupling neural collapse via hyperspherical uniformity gap,” in
2023
Closest in time.
2023
Closest in time.
2023
Closest in time.
A. Rangamani, M. Lindegaard, T. Galanti, and T. Poggio, “Feature learning in deep classifiers through intermediate neural collapse,” tech. rep., Center for Brains, Minds and Machines (CBMM), 2023
2023
Closest in time.
2023
Closest in time.
Z. Wang, Y. Luo, L. Zheng, Z. Huang, and M. Baktashmotlagh, “How far pre-trained models are from neural collapse on the target dataset informs their transferability,” in
2023
Closest in time.
H. Liu, C. Li, Q. Wu, and Y. J. Lee, “Visual instruction tuning,”
2023
Closest in time.
2023
Closest in time.
X. He, C. Li, P. Zhang, J. Yang, and X. E. Wang, “Parameter-efficient model adaptation for vision transformers,” in
2023
Closest in time.
Q. Zhang, M. Chen, A. Bukharin, P. He, Y. Cheng, W. Chen, and T. Zhao, “Adaptive budget allocation for parameter-efficient fine-tuning,” in
2023
Closest in time.
2023
Closest in time.
N. Timor, G. Vardi, and O. Shamir, “Implicit regularization towards rank minimization in relu networks,” in
2023
Closest in time.