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Large-scale multi-modal contrastive learning frameworks like CLIP typically require a large amount of image-text samples for training.
I-divergence geometry of probability distributions and minimization problems
Csiszár, I · 1975
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Catastrophic interference in connectionist networks: The sequential learning problem
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A lifelong learning perspective for mobile robot control
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
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From image descriptions to visual denotations: New similarity metrics for semantic inference over event descriptions
Young, P., Lai, A., Hodosh, M., and Hockenmaier, J · 2014
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Overcoming catastrophic forgetting in neural networks
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Learning without forgetting
Li, Z. and Hoiem, D · 2017
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Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
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icarl: Incremental classifier and representation learning
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert, C. H · 2017
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Representation learning with contrastive predictive coding
Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Conceptual captions: A cleaned, hypernymed, image alt-text dataset for automatic image captioning
Sharma, P., Ding, N., Goodman, S., and Soricut, R · 2018
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Uncertainty-based continual learning with adaptive regularization
Ahn, H., Cha, S., Lee, D., and Moon, T · 2019
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Experience replay for continual learning
Rolnick, D., Ahuja, A., Schwarz, J., Lillicrap, T., and Wayne, G · 2019
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Lamol: Language modeling for lifelong language learning
Sun, F.-K., Ho, C.-H., and Lee, H.-Y · 2019
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wav2vec 2.0: A framework for self-supervised learning of speech representations
Baevski, A., Zhou, Y., Mohamed, A., and Auli, M · 2020
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Continual lifelong learning in natural language processing: A survey
Biesialska, M., Biesialska, K., and Costa-Jussa, M. R · 2020
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Dark experience for general continual learning: a strong, simple baseline
Buzzega, P., Boschini, M., Porrello, A., Abati, D., and Calderara, S · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al · 2021
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Laion-400m: Open dataset of clip-filtered 400 million image-text pairs
Schuhmann, C., Vencu, R., Beaumont, R., Kaczmarczyk, R., Mullis, C., Katta, A., Coombes, T., Jitsev, J., and Komatsuzaki, A · 2021
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Does continual learning= catastrophic forgetting?
Thai, A., Stojanov, S., Rehg, I., and Rehg, J. M · 2021
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Detco: Unsupervised contrastive learning for object detection
Xie, E., Ding, J., Wang, W., Zhan, X., Xu, H., Sun, P., Li, Z., and Luo, P · 2021
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Der: Dynamically expandable representation for class incremental learning
Yan, S., Xie, J., and He, X · 2021
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Podnet: Pooled outputs distillation for small-tasks incremental learning
Douillard, A., Cord, M., Ollion, C., Robert, T., and Valle, E · 2020
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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2020
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Ss-il: Separated softmax for incremental learning
Ahn, H., Kwak, J., Lim, S., Bang, H., Kim, H., and Moon, T · 2021
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Ssul: Semantic segmentation with unknown label for exemplar-based class-incremental learning
Cha, S., Yoo, Y., Moon, T., et al · 2021
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Conceptual 12m: Pushing web-scale image-text pre-training to recognize long-tail visual concepts
Changpinyo, S., Sharma, P., Ding, N., and Soricut, R · 2021
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A continual learning survey: Defying forgetting in classification tasks
De Lange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T · 2021
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How well does self-supervised pre-training perform with streaming data?
Hu, D., Yan, S., Lu, Q., Lanqing, H., Hu, H., Zhang, Y., Li, Z., Wang, X., and Feng, J · 2021
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M6-T: exploring sparse expert models and beyond
Yang, A., Lin, J., Men, R., Zhou, C., Jiang, L., Jia, X., Wang, A., Zhang, J., Wang, J., Li, Y., Zhang, D., Lin, W., Qu, L., Zhou, J., and Yang, H · 2021
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Barlow twins: Self-supervised learning via redundancy reduction
Zbontar, J., Jing, L., Misra, I., LeCun, Y., and Deny, S · 2021
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Robust cross-modal representation learning with progressive self-distillation
Andonian, A., Chen, S., and Hamid, R · 2022
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Novelty controlled paraphrase generation with retrieval augmented conditional prompt tuning
Chowdhury, J. R., Zhuang, Y., and Wang, S · 2022
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A unified continuous learning framework for multi-modal knowledge discovery and pre-training
Fan, Z., Wei, Z., Chen, J., Wang, S., Li, Z., Xu, J., and Huang, X · 2022
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Test-time prompt tuning for zero-shot generalization in vision-language models
Shu, M., Nie, W., Huang, D.-A., Yu, Z., Goldstein, T., Anandkumar, A., and Xiao, C · 2022
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Climb: A continual learning benchmark for vision-and-language tasks
Srinivasan, T., Chang, T.-Y., Alva, L. L. P., Chochlakis, G., Rostami, M., and Thomason, J · 2022
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Learning to prompt for continual learning
Wang, Z., Zhang, Z., Lee, C.-Y., Zhang, H., Sun, R., Ren, X., Su, G., Perot, V., Dy, J., and Pfister, T · 2022
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Mvp: Multimodality-guided visual pre-training
Wei, L., Xie, L., Zhou, W., Li, H., and Tian, Q · 2022
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