Fetching the paper…
Reading the bibliography…
Modern deep neural networks are well known to be brittle in the face of unknown data instances and recognition of the latter remains a challenge.
Catastrophic Interference in Connectionist Networks: The Sequential Learning Problem
McCloskey, M.; Cohen, N.J · 1989
Earlier work this paper cites.
Handwritten Character Recognition Using Neural Network Architectures
Matan, O.; Kiang, R.; Stenard, C.E.; Boser, B.E.; Denker, J.; Henderson, D.; Hubbard, W.; Jackel, L.; LeCun, Y · 1990
Earlier work this paper cites.
Connectionist Models of Recognition Memory: Constraints Imposed by Learning and Forgetting Functions
Ratcliff, R · 1990
Earlier work this paper cites.
A Practical Bayesian Framework
MacKay, D.J.C · 1992
Earlier work this paper cites.
Catastrophic Forgetting, Rehearsal and Pseudorehearsal
Robins, A · 1995
Earlier work this paper cites.
Long Short-Term Memory
Hochreiter, S.; Schmidhuber, J · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
LeCun, Y.; Bottou, L.; Bengio, Y.; Haffner, P · 1998
Earlier work this paper cites.
Hippocampal and neocortical contributions to memory: Advances in the complementary learning systems framework
O’Reilly, R.C.; Norman, K.A · 2003
Earlier work this paper cites.
A Visual Vocabulary For Flower Classification. In Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR), Washington, DC, USA, 17–22 June 2006; pp. 1447–1454
Nilsback, M.E.; Zisserman, A · 2006
Earlier work this paper cites.
Herding dynamical weights to learn
Welling, M · 2009
Earlier work this paper cites.
Practical variational inference for neural networks
Graves, A · 2011
Earlier work this paper cites.
Reading Digits in Natural Images with Unsupervised Feature Learning. In Proceedings of the Neural Information Processing Systems (NeurIPS), Granada, Spain, 12–17 December 2011
Netzer, Y.; Wang, T.; Coates, A.; Bissacco, A.; Wu, B.; Ng, A.Y · 2011
Earlier work this paper cites.
Metric Learning for Large Scale Image Classification: Generalizing to New Classes at Near-Zero Cost
Mensink, T.; Verbeek, J.; Perronnin, F.; Csurka, G.; Mensink, T.; Verbeek, J.; Perronnin, F.; Csurka, G · 2012
Earlier work this paper cites.
Towards Open Set Recognition
Scheirer, W.J.; Rocha, A.; Sapkota, A.; Boult, T.E · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes
Kingma, D.P.; Welling, M · 2013
Earlier work this paper cites.
Probability Models For Open Set Recognition
Scheirer, W.J.; Jain, L.P.; Boult, T.E · 2014
Earlier work this paper cites.
Distilling the Knowledge in a Neural Network
Hinton, G.E.; Vinyals, O.; Dean, J · 2014
Earlier work this paper cites.
Generative Adversarial Nets
Goodfellow, I.J.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; Bengio, Y · 2014
Earlier work this paper cites.
Semi-Supervised Learning with Deep Generative Models
Kingma, D.P.; Rezende, D.J.; Mohamed, S.; Welling, M · 2014
Earlier work this paper cites.
Adam: A Method for Stochastic Optimization
Kingma, D.P.; Ba, J.L · 2015
Earlier work this paper cites.
Coresets for Nonparametric Estimation—The Case of DP-Means
Bachem, O.; Lucic, M.; Krause, A · 2015
Earlier work this paper cites.
Gal, Y.; Ghahramani, Z. Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning. In Proceedings of the International Conference on Machine Learning (ICML), Lille, France, 6–11 July 2015
2015
Earlier work this paper cites.
Towards Open World Recognition. In Proceedings of the 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Boston, MA, USA, 7–12 June 2015
Bendale, A.; Boult, T.E · 2015
Earlier work this paper cites.
Batch normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S.; Szegedy, C · 2015
Earlier work this paper cites.
Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
He, K.; Zhang, X.; Ren, S.; Sun, J · 2015
Earlier work this paper cites.
Lifelong Machine Learning. In
Chen, Z.; Liu, B · 2016
Cited alongside, same era.
Towards Open Set Deep Networks. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA,
Bendale, A.; Boult, T.E · 2016
Cited alongside, same era.
Pixel Recurrent Neural Networks
van den Oord, A.; Kalchbrenner, N.; Kavukcuoglu, K · 2016
Cited alongside, same era.
Learning without forgetting
Li, Z.; Hoiem, D · 2016
Cited alongside, same era.
A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems
Gepperth, A.; Karaoguz, C · 2016
Cited alongside, same era.
ELBO surgery: Yet another way to carve up the variational evidence lower bound
Hoffman, M.D.; Johnson, M.J · 2016
Cited alongside, same era.
Training Confidence-Calibrated Classifiers for Detecting Out-of-Distribution Samples
Lee, K.; Lee, H.; Lee, K.; Shin, J · 2018
Later among the works it cites.
Reducing Network Agnostophobia
Dhamija, A.R.; Günther, M.; Boult, T.E · 2018
Later among the works it cites.
VAE with a vampprior
Tomczak, J.M.; Welling, M · 2018
Later among the works it cites.
Interpreting and Explaining Deep Neural Networks for Classification of Audio Signals
Becker, S.; Ackermann, M.; Lapuschkin, S.; Müller, K.R.; Samek, W · 2018
Later among the works it cites.
Deep Learning for Classical Japanese Literature
Clanuwat, T.; Bober-Irizar, M.; Kitamoto, A.; Lamb, A.; Yamamoto, K.; Ha, D · 2018
Later among the works it cites.
Kemker, R.; McClure, M.; Abitino, A.; Hayes, T.; Kanan, C. Measuring Catastrophic Forgetting in Neural Networks. In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), New Orleans, LA, USA, 2–7 February 2018
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Wide Residual Networks
Zagoruyko, S.; Komodakis, N · 2016
Cited alongside, same era.
Deep Residual Learning for Image Recognition. In Proceedings of the 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, 26 June–1 July 2016
He, K.; Zhang, X.; Ren, S.; Sun, J · 2016
Cited alongside, same era.
Autoencoding beyond pixels using a learned similarity metric
Larsen, A.B.L.; Sonderby, S.K.; Larochelle, H.; Winther, O · 2016
Cited alongside, same era.
PixelVAE: A Latent Variable Model for Natural Images
Gulrajani, I.; Kumar, K.; Faruk, A.; Taiga, A.A.; Visin, F.; Vazquez, D.; Courville, A · 2017
Cited alongside, same era.
Continual Learning Through Synaptic Intelligence
Zenke, F.; Poole, B.; Ganguli, S · 2017
Cited alongside, same era.
Overcoming catastrophic forgetting in neural networks
Kirkpatrick, J.; Pascanu, R.; Rabinowitz, N.; Veness, J.; Desjardins, G.; Rusu, A.A.; Milan, K.; Quan, J.; Ramalho, T.; · 2017
Cited alongside, same era.
2018
Later among the works it cites.
Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence
Chaudhry, A.; Dokania, P.K.; Ajanthan, T.; Torr, P.H.S · 2018
Later among the works it cites.
Memory Replay GANs: Learning to generate images from new categories without forgetting
Wu, C.; Herranz, L.; Liu, X.; Wang, Y.; van de Weijer, J.; Raducanu, B · 2018
Later among the works it cites.
Learning and the Unknown: Surveying Steps Toward Open World Recognition
Boult, T.E.; Cruz, S.; Dhamija, A.R.; Gunther, M.; Henrydoss, J.; Scheirer, W.J · 2019
Closest in time.
Benchmarking neural network robustness to common corruptions and perturbations
Hendrycks, D.; Dietterich, T · 2019
Closest in time.
Can You Trust Your Model’s Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift
Ovadia, Y.; Fertig, E.; Ren, J.; Nado, Z.; Sculley, D.; Nowozin, S.; Dillon, J.V.; Lakshminarayanan, B.; Snoek, J · 2019
Closest in time.
Do Deep Generative Models Know What They Don’t Know?
Nalisnick, E.; Matsukawa, A.; Teh, Y.W.; Gorur, D.; Lakshminarayanan, B · 2019
Closest in time.
Continual Lifelong Learning with Neural Networks: A Review
Parisi, G.I.; Kemker, R.; Part, J.L.; Kanan, C.; Wermter, S · 2019
Closest in time.
Adversarial Examples are not Bugs, they are Features
Ilyas, A.; Santurkar, S.; Tsipras, D.; Engstrom, L.; Tran, B.; Madry, A · 2019
Closest in time.
Disentangling disentanglement in variational autoencoders
Mathieu, E.; Rainforth, T.; Siddharth, N.; Teh, Y.W · 2019
Closest in time.
Resampled Priors for Variational Autoencoders
Bauer, M.; Mnih, A · 2019
Closest in time.
Variational Autoencoder with Implicit Optimal Priors
Takahashi, H.; Iwata, T.; Yamanaka, Y.; Yamada, M.; Yagi, S · 2019
Closest in time.
Overcoming catastrophic forgetting for continual learning via model adaptation
Hu, W.; Lin, Z.; Liu, B.; Tao, C.; Tao, Z.; Zhao, D.; Ma, J.; Yan, R · 2019
Closest in time.
Lifelong GAN: Continual Learning for Conditional Image Generation
Zhai, M.; Chen, L.; Tung, F.; He, J.; Nawhal, M.; Mori, G · 2019
Closest in time.
The Pitfalls of Simplicity Bias in Neural Networks
Shah, H.; Tamuly, K.; Raghunathan, A.; Jain, P.; Netrapalli, P · 2020
Closest in time.
GDumb: A Simple Approach that Questions Our Progress in Continual Learning
Prabhu, A.; Torr, P.; Dokania, P · 2020
Closest in time.
Mnemonics Training: Multi-Class Incremental Learning without Forgetting
Liu, Y.; Su, Y.; Liu, A.A.; Schiele, B.; Sun, Q · 2020
Closest in time.
Dark Experience for General Continual Learning: A Strong, Simple Baseline
Buzzega, P.; Boschini, M.; Porrello, A.; Abati, D.; Calderara, S · 2020
Closest in time.
Co2L: Contrastive Continual Learning
Cha, H.; Lee, J.; Shin, J · 2021
Closest in time.
Learning Multiple Layers of Features from Tiny Images; Technical Report, Toronto. 2009. Available online:
Krizhevsky, A · 2022
Closest in time.