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When training overparameterized deep networks for classification tasks, it has been widely observed that the learned features exhibit a so-called "neural collapse" phenomenon.
Approximation capabilities of multilayer feedforward networks
K. Hornik · 1991
Earlier work this paper cites.
Optimization algorithms on matrix manifolds
P.-A. Absil, R. Mahony, and R. Sepulchre · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
Earlier work this paper cites.
MNIST handwritten digit database. AT&T labs, 2010
Y. LeCun, C. Cortes, and C. Burges · 2010
Earlier work this paper cites.
Finding a sparse vector in a subspace: Linear sparsity using alternating directions
Q. Qu, J. Sun, and J. Wright · 2014
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
Earlier work this paper cites.
Escaping from saddle points—online stochastic gradient for tensor decomposition
R. Ge, F. Huang, C. Jin, and Y. Yuan · 2015
Earlier work this paper cites.
When are nonconvex problems not scary?
J. Sun, Q. Qu, and J. Wright · 2015
Earlier work this paper cites.
Rethinking the inception architecture for computer vision, 2015
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2015
Earlier work this paper cites.
Deep neural networks for youtube recommendations
P. Covington, J. Adams, and E. Sargin · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Earlier work this paper cites.
Large-margin softmax loss for convolutional neural networks
W. Liu, Y. Wen, Z. Yu, and M. Yang · 2016
Earlier work this paper cites.
Complete dictionary recovery over the sphere I: Overview and the geometric picture
J. Sun, Q. Qu, and J. Wright · 2016
Earlier work this paper cites.
A discriminative feature learning approach for deep face recognition
Y. Wen, K. Zhang, Z. Li, and Y. Qiao · 2016
Earlier work this paper cites.
Understanding deep learning requires rethinking generalization
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2016
Earlier work this paper cites.
Gradient descent can take exponential time to escape saddle points
S. S. Du, C. Jin, J. D. Lee, M. I. Jordan, A. Singh, and B. Poczos · 2017
Earlier work this paper cites.
Densely connected convolutional networks
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger · 2017
Earlier work this paper cites.
How to escape saddle points efficiently
C. Jin, M. Jordan, R. Ge, P. Netrapalli, and S. Kakade · 2017
Earlier work this paper cites.
Focal loss for dense object detection, 2017
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár · 2017
Earlier work this paper cites.
Sphereface: Deep hypersphere embedding for face recognition
W. Liu, Y. Wen, Z. Yu, M. Li, B. Raj, and L. Song · 2017
Earlier work this paper cites.
The expressive power of neural networks: a view from the width
Z. Lu, H. Pu, F. Wang, Z. Hu, and L. Wang · 2017
Earlier work this paper cites.
L2-constrained softmax loss for discriminative face verification
R. Ranjan, C. D. Castillo, and R. Chellappa · 2017
Earlier work this paper cites.
Complete dictionary recovery over the sphere II: Recovery by Riemannian trust
J. Sun, Q. Qu, and J. Wright · 2017
Earlier work this paper cites.
Normface: L2 hypersphere embedding for face verification
F. Wang, X. Xiang, J. Cheng, and A. L. Yuille · 2017
Earlier work this paper cites.
Deep linear networks with arbitrary loss: All local minima are global
T. Laurent and J. Brecht · 2018
Earlier work this paper cites.
Provable approximation properties for deep neural networks
U. Shaham, A. Cloninger, and R. R. Coifman · 2018
Earlier work this paper cites.
Theoretical insights into the optimization landscape of over-parameterized shallow neural networks
M. Soltanolkotabi, A. Javanmard, and J. D. Lee · 2018
Earlier work this paper cites.
Cosface: Large margin cosine loss for deep face recognition
H. Wang, Y. Wang, Z. Zhou, X. Ji, D. Gong, J. Zhou, Z. Li, and W. Liu · 2018
Earlier work this paper cites.
Pre-training tasks for embedding-based large-scale retrieval
W.-C. Chang, X. Y. Felix, Y.-W. Chang, Y. Yang, and S. Kumar · 2019
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Efficiently escaping saddle points on manifolds
C. Criscitiello and N. Boumal · 2019
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Arcface: Additive angular margin loss for deep face recognition
J. Deng, J. Guo, N. Xue, and S. Zafeiriou · 2019
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Analyzing and improving representations with the soft nearest neighbor loss
N. Frosst, N. Papernot, and G. Hinton · 2019
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Geometry and symmetry in short-and-sparse deconvolution
H.-W. Kuo, Y. Lau, Y. Zhang, and J. Wright · 2019
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Maximally compact and separated features with regular polytope networks
F. Pernici, M. Bruni, C. Baecchi, and A. Del Bimbo · 2019
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Understanding contrastive representation learning through alignment and uniformity on the hypersphere
T. Wang and P. Isola · 2020
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S. Wojtowytsch et al · 2020
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Y. Yu, K. H. R. Chan, C. You, C. Song, and Y. Ma · 2020
Later among the works it cites.
From symmetry to geometry: Tractable nonconvex problems
Y. Zhang, Q. Qu, and J. Wright · 2020
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Redunet: A white-box deep network from the principle of maximizing rate reduction
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Dimensionality compression and expansion in deep neural networks
S. Recanatesi, M. Farrell, M. Advani, T. Moore, G. Lajoie, and E. Shea-Brown · 2019
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Escaping from saddle points on riemannian manifolds
Y. Sun, N. Flammarion, and M. Fazel · 2019
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Structured local optima in sparse blind deconvolution
Y. Zhang, H.-W. Kuo, and J. Wright · 2019
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Deep networks from the principle of rate reduction
K. H. R. Chan, Y. Yu, C. You, H. Qi, J. Wright, and Y. Ma · 2020
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A simple framework for contrastive learning of visual representations
T. Chen, S. Kornblith, M. Norouzi, and G. Hinton · 2020
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Separability and geometry of object manifolds in deep neural networks
U. Cohen, S. Chung, D. D. Lee, and H. Sompolinsky · 2020
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K. H. R. Chan, Y. Yu, C. You, H. Qi, J. Wright, and Y. Ma · 2021
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Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
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Layer-peeled model: Toward understanding well-trained deep neural networks
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Dissecting supervised constrastive learning
F. Graf, C. Hofer, M. Niethammer, and R. Kwitt · 2021
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Neural collapse in deep homogeneous classifiers and the role of weight decay
F. Graf, C. Hofer, M. Niethammer, and R. Kwitt · 2021
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Neural collapse under mse loss: Proximity to and dynamics on the central path
X. Han, V. Papyan, and D. L. Donoho · 2021
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An unconstrained layer-peeled perspective on neural collapse
W. Ji, Y. Lu, Y. Zhang, Z. Deng, and W. J. Su · 2021
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Why do better loss functions lead to less transferable features?
S. Kornblith, T. Chen, H. Lee, and M. Norouzi · 2021
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Regular polytope networks
F. Pernici, M. Bruni, C. Baecchi, and A. Del Bimbo · 2021
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Dynamics and neural collapse in deep classifiers trained with the square loss
A. Rangamani, M. Xu, A. Banburski, Q. Liao, and T. Poggio · 2021
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When expressivity meets trainability: Fewer than
J. Zhang, Y. Zhang, M. Hong, R. Sun, and Z.-Q. Luo · 2021
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A geometric analysis of neural collapse with unconstrained features
Z. Zhu, T. Ding, J. Zhou, X. Li, C. You, J. Sulam, and Q. Qu · 2021
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Nearest class-center simplification through intermediate layers
I. Ben-Shaul and S. Dekel · 2022
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An introduction to optimization on smooth manifolds
N. Boumal · 2022
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On the role of neural collapse in transfer learning
T. Galanti, A. György, and M. Hutter · 2022
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Limitations of neural collapse for understanding generalization in deep learning
L. Hui, M. Belkin, and P. Nakkiran · 2022
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Neural collapse: A review on modelling principles and generalization
V. Kothapalli, E. Rasromani, and V. Awatramani · 2022
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Loss landscapes and optimization in over-parameterized non-linear systems and neural networks
C. Liu, L. Zhu, and M. Belkin · 2022
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Extended unconstrained features model for exploring deep neural collapse
T. Tirer and J. Bruna · 2022
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Convergence and recovery guarantees of the k-subspaces method for subspace clustering
P. Wang, H. Liu, A. M.-C. So, and L. Balzano · 2022
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Neural collapse inspired attraction-repulsion-balanced loss for imbalanced learning
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Do we really need a learnable classifier at the end of deep neural network?
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Towards discriminative representation: Multi-view trajectory contrastive learning for online multi-object tracking
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