Fetching the paper…
Reading the bibliography…
Online continual learning suffers from an underfitted solution due to insufficient training for prompt model update (e.g., single-epoch training).
Learning image transformations without training examples
Sergey Pankov · 2011
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction
Carl Doersch, Abhinav Gupta, and Alexei A Efros · 2015
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik Kingma and Jimmy Ba · 2015
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
Andrei A Rusu, Neil C Rabinowitz, Guillaume Desjardins, Hubert Soyer, James Kirkpatrick, Koray Kavukcuoglu, Razvan Pascanu, and Raia Hadsell · 2016
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Learning without forgetting
Zhizhong Li and Derek Hoiem · 2017
Earlier work this paper cites.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Earlier work this paper cites.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Earlier work this paper cites.
Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
Earlier work this paper cites.
Equiangular tight frames that contain regular simplices
Matthew Fickus, John Jasper, Emily J King, and Dustin G Mixon · 2018
Earlier work this paper cites.
Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
Earlier work this paper cites.
Task-free continual learning
Rahaf Aljundi, Klaas Kelchtermans, and Tinne Tuytelaars · 2019
Earlier work this paper cites.
Superposition of many models into one
Brian Cheung, Alexander Terekhov, Yubei Chen, Pulkit Agrawal, and Bruno Olshausen · 2019
Earlier work this paper cites.
Self-supervised representation learning by rotation feature decoupling
Zeyu Feng, Chang Xu, and Dacheng Tao · 2019
Earlier work this paper cites.
Regularization shortcomings for continual learning
Timothée Lesort, Andrei Stoian, and David Filliat · 2019
Earlier work this paper cites.
Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
Earlier work this paper cites.
Experience replay for continual learning
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Earlier work this paper cites.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
Earlier work this paper cites.
Cutmix: Regularization strategy to train strong classifiers with localizable features
Sangdoo Yun, Dongyoon Han, Seong Joon Oh, Sanghyuk Chun, Junsuk Choe, and Youngjoon Yoo · 2019
Earlier work this paper cites.
Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2020
Earlier work this paper cites.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Earlier work this paper cites.
Counter-examples generation from a positive unlabeled image dataset
Florent Chiaroni, Ghazaleh Khodabandelou, Mohamed-Cherif Rahal, Nicolas Hueber, and Frederic Dufaux · 2020
Earlier work this paper cites.
Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
Cited alongside, same era.
Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, and Christopher Kanan · 2020
Cited alongside, same era.
Neural collapse with cross-entropy loss
Jianfeng Lu and Stefan Steinerberger · 2020
Cited alongside, same era.
Neural collapse with unconstrained features
Dustin G Mixon, Hans Parshall, and Jianzong Pi · 2020
Cited alongside, same era.
Prevalence of neural collapse during the terminal phase of deep learning training
Vardan Papyan, XY Han, and David L Donoho · 2020
Cited alongside, same era.
On anytime learning at macroscale
Lucas Caccia, Jing Xu, Myle Ott, Marcaurelio Ranzato, and Ludovic Denoyer · 2022
Later among the works it cites.
Continual learning beyond a single model
Thang Doan, Seyed Iman Mirzadeh, and Mehrdad Farajtabar · 2022
Later among the works it cites.
Pooling revisited: Your receptive field is suboptimal
Dong-Hwan Jang, Sanghyeok Chu, Joonhyuk Kim, and Bohyung Han · 2022
Later among the works it cites.
Continual learning based on ood detection and task masking
Gyuhak Kim, Sepideh Esmaeilpour, Changnan Xiao, and Bing Liu · 2022
Later among the works it cites.
Objectmix: Data augmentation by copy-pasting objects in videos for action recognition
Jun Kimata, Tomoya Nitta, and Toru Tamaki · 2022
Later among the works it cites.
Online continual learning on class incremental blurry task configuration with anytime inference
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Latent replay for real-time continual learning
Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, and Davide Maltoni · 2020
Cited alongside, same era.
Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip HS Torr, and Puneet K Dokania · 2020
Cited alongside, same era.
Rainbow memory: Continual learning with a memory of diverse samples
Jihwan Bang, Heesu Kim, YoungJoon Yoo, Jung-Woo Ha, and Jonghyun Choi · 2021
Cited alongside, same era.
New insights on reducing abrupt representation change in online continual learning
Lucas Caccia, Rahaf Aljundi, Nader Asadi, Tinne Tuytelaars, Joelle Pineau, and Eugene Belilovsky · 2021
Cited alongside, same era.
Online continual learning with natural distribution shifts: An empirical study with visual data
Zhipeng Cai, Ozan Sener, and Vladlen Koltun · 2021
Cited alongside, same era.
Exploring deep neural networks via layer-peeled model: Minority collapse in imbalanced training
Cong Fang, Hangfeng He, Qi Long, and Weijie J Su · 2021
Cited alongside, same era.
An unconstrained layer-peeled perspective on neural collapse
Wenlong Ji, Yiping Lu, Yiliang Zhang, Zhun Deng, and Weijie J Su · 2021
Cited alongside, same era.
Hyunseo Koh, Dahyun Kim, Jung-Woo Ha, and Jonghyun Choi · 2022
Later among the works it cites.
Sagemix: Saliency-guided mixup for point clouds
Sanghyeok Lee, Minkyu Jeon, Injae Kim, Yunyang Xiong, and Hyunwoo J Kim · 2022
Later among the works it cites.
Few-shot class-incremental learning from an open-set perspective
Can Peng, Kun Zhao, Tianren Wang, Meng Li, and Brian C Lovell · 2022
Later among the works it cites.
Neural collapse in deep homogeneous classifiers and the role of weight decay
Akshay Rangamani and Andrzej Banburski-Fahey · 2022
Later among the works it cites.
Extended unconstrained features model for exploring deep neural collapse
Tom Tirer and Joan Bruna · 2022
Later among the works it cites.
Inducing neural collapse in imbalanced learning: Do we really need a learnable classifier at the end of deep neural network?
Yibo Yang, Shixiang Chen, Xiangtai Li, Liang Xie, Zhouchen Lin, and Dacheng Tao · 2022
Later among the works it cites.
Task-free continual learning via online discrepancy distance learning
Fei Ye and Adrian G Bors · 2022
Later among the works it cites.
Verse: Virtual-gradient aware streaming lifelong learning with anytime inference
Soumya Banerjee, Vinay K Verma, Avideep Mukherjee, Deepak Gupta, Vinay P Namboodiri, and Piyush Rai · 2023
Later among the works it cites.
Online bias correction for task-free continual learning
Aristotelis Chrysakis and Marie-Francine Moens · 2023
Later among the works it cites.
Continual learning of generative models with limited data: From wasserstein-1 barycenter to adaptive coalescence
Mehmet Dedeoglu, Sen Lin, Zhaofeng Zhang, and Junshan Zhang · 2023
Later among the works it cites.
Real-time evaluation in online continual learning: A new hope
Yasir Ghunaim, Adel Bibi, Kumail Alhamoud, Motasem Alfarra, Hasan Abed Al Kader Hammoud, Ameya Prabhu, Philip HS Torr, and Bernard Ghanem · 2023
Later among the works it cites.
Generative negative replay for continual learning
Gabriele Graffieti, Davide Maltoni, Lorenzo Pellegrini, and Vincenzo Lomonaco · 2023
Later among the works it cites.
Online boundary-free continual learning by scheduled data prior
Hyunseo Koh, Minhyuk Seo, Jihwan Bang, Hwanjun Song, Deokki Hong, Seulki Park, Jung-Woo Ha, and Jonghyun Choi · 2023
Later among the works it cites.
Continual learning with invertible generative models
Jary Pomponi, Simone Scardapane, and Aurelio Uncini · 2023
Later among the works it cites.
Budgeted online continual learning by adaptive layer freezing and frequency-based sampling
Minhyuk Seo, Hyunseo Koh, and Jonghyun Choi · 2023
Later among the works it cites.
Temporal alignment of human motion data: A geometric point of view
Alice Barbora Tumpach and Peter Kán · 2023
Later among the works it cites.
Rethinking class imbalance in machine learning
Ou Wu · 2023
Later among the works it cites.
Understanding imbalanced semantic segmentation through neural collapse
Zhisheng Zhong, Jiequan Cui, Yibo Yang, Xiaoyang Wu, Xiaojuan Qi, Xiangyu Zhang, and Jiaya Jia · 2023
Later among the works it cites.