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As AI agents are increasingly used in the real open world with unknowns or novelties, they need the ability to (1) recognize objects that (a) they have learned before and (b) detect items that they have never seen or learned, and (2) learn the new items incrementally to become more and more knowledgeable and powerful.
Towards open world recognition, in: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 1893–1902
Bendale, A., Boult, T., 2015 · 1902
Earlier work this paper cites.
Three scenarios for continual learning
van de Ven, G.M., Tolias, A.S., 2019 · 1904
Earlier work this paper cites.
Catastrophic interference in connectionist networks: The sequential learning problem, in: Psychology of learning and motivation. Elsevier. volume 24, pp. 109–165
McCloskey, M., Cohen, N.J., 1989 · 1989
Earlier work this paper cites.
Support vector method for novelty detection., in: NIPS, Citeseer. pp. 582–588
Schölkopf, B., Williamson, R.C., Smola, A.J., Shawe-Taylor, J., Platt, J.C., et al., 1999 · 1999
Earlier work this paper cites.
Supervised contrastive learning
Khosla, P., Teterwak, P., Wang, C., Sarna, A., Tian, Y., Isola, P., Maschinot, A., Liu, C., Krishnan, D., 2020 · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Krizhevsky, A., Hinton, G., 2009 · 2009
Earlier work this paper cites.
A review of open-world learning and steps toward open-world learning without labels
Jafarzadeh, M., Dhamija, A.R., Cruz, S., Li, C., Ahmad, T., Boult, T.E., 2021 · 2011
Earlier work this paper cites.
A pac-bayesian bound for lifelong learning, in: International Conference on Machine Learning, PMLR. pp. 991–999
Pentina, A., Lampert, C., 2014 · 2014
Earlier work this paper cites.
Probability models for open set recognition
Scheirer, W.J., Jain, L.P., Boult, T.E., 2014 · 2014
Earlier work this paper cites.
Explaining and harnessing adversarial examples
Goodfellow, I.J., Shlens, J., Szegedy, C., 2015 · 2015
Earlier work this paper cites.
Tiny imagenet visual recognition challenge
Le, Y., Yang, X., 2015 · 2015
Earlier work this paper cites.
Imagenet large scale visual recognition challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al., 2015 · 2015
Earlier work this paper cites.
Going deeper with convolutions, in: CVPR
Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., Rabinovich, A., 2015 · 2015
Earlier work this paper cites.
Learning cumulatively to become more knowledgeable, in: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1565–1574
Fei, G., Wang, S., Liu, B., 2016 · 2016
Earlier work this paper cites.
Deep residual learning for image recognition, in: CVPR
He, K., Zhang, X., Ren, S., Sun, J., 2016 · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Hendrycks, D., Gimpel, K., 2016 · 2016
Earlier work this paper cites.
Learning Without Forgetting, in: ECCV, Springer. pp. 614–629
Li, Z., Hoiem, D., 2016 · 2016
Earlier work this paper cites.
Sgdr: Stochastic gradient descent with warm restarts
Loshchilov, I., Hutter, F., 2016 · 2016
Earlier work this paper cites.
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., Grabska-Barwinska, A., Others, 2017 · 2017
Earlier work this paper cites.
Gradient Episodic Memory for Continual Learning, in: NeurIPS, pp. 6470–6479
Lopez-Paz, D., Ranzato, M., 2017 · 2017
Earlier work this paper cites.
iCaRL: Incremental classifier and representation learning, in: CVPR, pp. 5533–5542
Rebuffi, S.A., Kolesnikov, A., Lampert, C.H., 2017 · 2017
Earlier work this paper cites.
Continual learning with deep generative replay, in: NIPS, pp. 2994–3003
Shin, H., Lee, J.K., Kim, J., Kim, J., 2017 · 2017
Earlier work this paper cites.
Large batch training of convolutional networks
You, Y., Gitman, I., Ginsburg, B., 2017 · 2017
Earlier work this paper cites.
Continual learning through synaptic intelligence, in: ICML, pp. 3987–3995
Zenke, F., Poole, B., Ganguli, S., 2017 · 2017
Earlier work this paper cites.
End-to-end incremental learning, in: Proceedings of the European conference on computer vision (ECCV)
Castro, F.M., Marín-Jiménez, M.J., Guil, N., Schmid, C., Alahari, K., 2018 · 2018
Earlier work this paper cites.
Efficient lifelong learning with a-gem
Chaudhry, A., Ranzato, M., Rohrbach, M., Elhoseiny, M., 2018 · 2018
Cited alongside, same era.
Lifelong machine learning
Chen, Z., Liu, B., 2018 · 2018
Cited alongside, same era.
A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Lee, K., Lee, K., Lee, H., Shin, J., 2018 · 2018
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks, in: ICLR
Liang, S., Li, Y., Srikant, R., 2018 · 2018
Cited alongside, same era.
Overcoming catastrophic forgetting with hard attention to the task, in: International Conference on Machine Learning, PMLR. pp. 4548–4557
Serra, J., Suris, D., Miron, M., Karatzoglou, A., 2018 · 2018
Cited alongside, same era.
{EEC}: Learning to encode and regenerate images for continual learning, in: International Conference on Learning Representations
Ayub, A., Wagner, A., 2021 · 2021
Later among the works it cites.
Rainbow memory: Continual learning with a memory of diverse samples, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 8218–8227
Bang, J., Kim, H., Yoo, Y., Ha, J.W., Choi, J., 2021 · 2021
Later among the works it cites.
Co2l: Contrastive continual learning, in: ICCV
Cha, H., Lee, J., Shin, J., 2021 · 2021
Later among the works it cites.
Using hindsight to anchor past knowledge in continual learning
Chaudhry, A., Gordo, A., Dokania, P., Torr, P., Lopez-Paz, D., 2021 · 2021
Later among the works it cites.
Posterior meta-replay for continual learning
Henning, C., Cervera, M., D’Angelo, F., Von Oswald, J., Traber, R., Ehret, B., Kobayashi, S., Grewe, B.F., Sacramento, J., 2021 · 2021
Later among the works it cites.
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Online continual learning with maximal interfered retrieval, in: NeurIPS
Aljundi, R., Belilovsky, E., Tuytelaars, T., Charlin, L., Caccia, M., Lin, M., Caccia, L., 2019 · 2019
Cited alongside, same era.
Continual learning with tiny episodic memories, in: Workshop on Multi-Task and Lifelong Reinforcement Learning
Chaudhry, A., Rohrbach, M., Elhoseiny, M., Ajanthan, T., Dokania, P., Torr, P., Ranzato, M., 2019 · 2019
Cited alongside, same era.
Learning to discover novel visual categories via deep transfer clustering, in: International Conference on Computer Vision (ICCV)
Han, K., Vedaldi, A., Zisserman, A., 2019 · 2019
Cited alongside, same era.
Using self-supervised learning can improve model robustness and uncertainty, in: NeurIPS, pp. 15663–15674
Hendrycks, D., Mazeika, M., Kadavath, S., Song, D., 2019 · 2019
Cited alongside, same era.
Parameter-efficient transfer learning for nlp, in: International Conference on Machine Learning, PMLR. pp. 2790–2799
Houlsby, N., Giurgiu, A., Jastrzebski, S., Morrone, B., De Laroussilhe, Q., Gesmundo, A., Attariyan, M., Gelly, S., 2019 · 2019
Cited alongside, same era.
Overcoming catastrophic forgetting with unlabeled data in the wild, in: CVPR
Lee, K., Lee, K., Shin, J., Lee, H., 2019 · 2019
Cited alongside, same era.
Learning to remember: A synaptic plasticity driven framework for continual learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11321–11329
Ostapenko, O., Puscas, M., Klein, T., Jahnichen, P., Nabi, M., 2019 · 2019
Cited alongside, same era.
Continual learning by using information of each class holistically, in: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 7797–7805
Hu, W., Qin, Q., Wang, M., Ma, J., Liu, B., 2021 · 2021
Later among the works it cites.
Achieving forgetting prevention and knowledge transfer in continual learning
Ke, Z., Liu, B., Ma, N., Xu, H., Shu, L., 2021 · 2021
Later among the works it cites.
Deep learning for anomaly detection: A review
Pang, G., Shen, C., Cao, L., Hengel, A.V.D., 2021 · 2021
Later among the works it cites.
Open-world machine learning: Applications, challenges, and opportunities
Parmar, J., Chouhan, S.S., Raychoudhury, V., Rathore, S.S., 2021 · 2021
Later among the works it cites.
Training data-efficient image transformers & distillation through attention, in: International Conference on Machine Learning, PMLR. pp. 10347–10357
Touvron, H., Cord, M., Douze, M., Massa, F., Sablayrolles, A., Jégou, H., 2021 · 2021
Later among the works it cites.
Class-incremental learning with generative classifiers, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3611–3620
Van De Ven, G.M., Li, Z., Tolias, A.S., 2021 · 2021
Later among the works it cites.
Der: Dynamically expandable representation for class incremental learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3014–3023
Yan, S., Xie, J., He, X., 2021 · 2021
Later among the works it cites.
Generalized out-of-distribution detection: A survey
Yang, J., Zhou, K., Li, Y., Liu, Z., 2021 · 2021
Later among the works it cites.
Prototype augmentation and self-supervision for incremental learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5871–5880
Zhu, F., Zhang, X.Y., Wang, C., Yin, F., Liu, C.L., 2021 · 2021
Later among the works it cites.
Shels: Exclusive feature sets for novelty detection and continual learning without class boundaries, in: Conference on Lifelong Learning Agents, PMLR. pp. 1065–1085
Gummadi, M., Kent, D., Mendez, J.A., Eaton, E., 2022 · 2022
Later among the works it cites.
Out-of-distribution detection in unsupervised continual learning, in: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 3850–3855
He, J., Zhu, F., 2022 · 2022
Later among the works it cites.
Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting, in: International Conference on Learning Representations
Karakida, R., Akaho, S., 2022 · 2022
Later among the works it cites.
Continual learning of natural language processing tasks: A survey
Ke, Z., Liu, B., 2022 · 2022
Later among the works it cites.
Continual learning based on ood detection and task masking, in: CVPR 2022 Workshop on Continual Learning
Kim, G., Esmaeilpour, S., Xiao, C., Liu, B., 2022 · 2022
Later among the works it cites.
Self-initiated open world learning for autonomous ai agents, in: Proceedings of AAAI Symposium on ‘Designing Artificial Intelligence for Open Worlds’
Liu, B., Robertson, E., Grigsby, S., Mazumder, S., 2022 · 2022
Later among the works it cites.
An integrated architecture for online adaptation to novelty in open worlds using probabilistic programming and novelty-aware planning, in: Proceedings of the AAAI Spring Symposium on Designing AI for Open-World Novelty
Loyall, B., Pfeffer, A., Niehaus, J., Mayer, T., Rizzo, P., Gee, A., Cvijic, S., Manning, W., Skitka, M.K., Becker, M., et al., 2022 · 2022
Later among the works it cites.
Continuous learning based novelty aware emotion recognition system
Palash, M., Bhargava, B., 2022 · 2022
Later among the works it cites.
incdfm: Incremental deep feature modeling for continual novelty detection, in: European Conference on Computer Vision, Springer. pp. 588–604
Rios, A., Ahuja, N., Ndiour, I., Genc, U., Itti, L., Tickoo, O., 2022 · 2022
Later among the works it cites.
An architecture for novelty handling in a multi-agent stochastic environment: Case study in open-world monopoly, in: Designing Artificial Intelligence for Open Worlds: Papers from the 2022 Spring Symposium, Virtual. AAAI Press
Thai, T., Shen, M., Varshney, N., Gopalakrishnan, S., Soni, U., Baral, C., Scheutz, M., Sinapov, J., 2022 · 2022
Later among the works it cites.
Ai autonomy: Self-initiated open-world continual learning and adaptation
Liu, B., Mazumder, S., Robertson, E., Grigsby, S., 2023 · 2023
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