[
Barrett, D., Hill, F., Santoro, A., Morcos, A. and Lillicrap, T. (2018). Measuring abstract reasoning in neural networks, in J. Dy and A. Krause (Eds), Proceedings of the 35th International Conference on Machine Learning, Proceedings of Machine Learning Research, Vol. 80, PMLR, Cambridge, pp. 511–520.
]Search in Google Scholar
[
Benny, Y., Pekar, N. and Wolf, L. (2021). Scale-localized abstract reasoning, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), Nashville, USA, pp. 12557–12565.
]Search in Google Scholar
[
Bongard, M. (1970). Pattern Recognition, Spartan Books, Baltimore.
]Search in Google Scholar
[
Defays, D. (1995). Numbo: A study in cognition and recognition, https://www.researchgate.net/publication/262363566_Numbo_a_study_in_cognition_and_recognition.
]Search in Google Scholar
[
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K. and Fei-Fei, L. (2009). Imagenet: A large-scale hierarchical image database, 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, pp. 248–255.
]Search in Google Scholar
[
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J. and Houlsby, N. (2020). An image is worth 16x16 words: Transformers for image recognition at scale, arXiv: 2010.11929.
]Search in Google Scholar
[
Hahne, L., Lüddecke, T., Wörgötter, F. and Kappel, D. (2019). Attention on abstract visual reasoning, CoRR: abs/1911.05990.
]Search in Google Scholar
[
Hofstadter, D.R. (1995). Fluid Concepts & Creative Analogies: Computer Models of the Fundamental Mechanisms of Thought, Basic Books, New York.
]Search in Google Scholar
[
Hu, S., Ma, Y., Liu, X., Wei, Y. and Bai, S. (2020). Hierarchical rule induction network for abstract visual reasoning, https://www.researchgate.net/publication/339324056_Hierarchical_Rule_Induction_Network_for_Abstract_Visual_Reasoning.
]Search in Google Scholar
[
Hu, S., Ma, Y., Liu, X., Wei, Y. and Bai, S. (2021). Stratified rule-aware network for abstract visual reasoning, Proceedings of the AAAI Conference on Artificial Intelligence, pp. 1567–1574, (virtual).
]Search in Google Scholar
[
Kim, Y., Shin, J., Yang, E. and Hwang, S.J. (2020). Few-shot visual reasoning with meta-analogical contrastive learning, in H. Larochelle et al. (Eds), Advances in Neural Information Processing Systems, Vol. 33, Curran Associates, Inc., Red Hook, pp. 16846–16856.
]Search in Google Scholar
[
Lei Ba, J., Kiros, J.R. and Hinton, G.E. (2016). Layer normalization, arXiv: 1607.06450.
]Search in Google Scholar
[
Luo, W., Li, Y., Urtasun, R. and Zemel, R. (2017). Understanding the effective receptive field in deep convolutional neural networks, arXiv: 1701.04128.
]Search in Google Scholar
[
Małkiński, M. and Mańdziuk, J. (2022a). Deep learning methods for abstract visual reasoning: A survey on Raven’s progressive matrices, arXiv: 2201.12382.
]Search in Google Scholar
[
Małkiński, M. and Mańdziuk, J. (2022b). Multi-label contrastive learning for abstract visual reasoning, IEEE Transactions on Neural Networks and Learning Systems 35(2): 1941–1953, DOI: 10.1109/TNNLS.2022.3185949.
]Search in Google Scholar
[
Raven, J.C. (1936). Mental Tests Used in Genetic, the Performance of Related Individuals on Tests Mainly Educative and Mainly Reproductive, MSc thesis, University of London, London.
]Search in Google Scholar
[
Spratley, S., Ehinger, K. and Miller, T. (2020). A closer look at generalisation in Raven, Computer Vision, ECCV 2020: 16th European Conference, Glasgow, UK, pp. 601–616, DOI: 10.1007/978-3-030-58583-9_36.
]Search in Google Scholar
[
Tan, M. and Le, Q. (2019). EfficientNet: Rethinking model scaling for convolutional neural networks, in K. Chaudhuri and R. Salakhutdinov (Eds), Proceedings of the 36th International Conference on Machine Learning, Proceedings of Machine Learning Research, Vol. 97, PMLR, Cambridge, pp. 6105–6114.
]Search in Google Scholar
[
Tan, M. and Le, Q.V. (2021). EfficientNetV2: Smaller models and faster training, in M. Meila and T. Zhang (Eds), Proceedings of the 38th International Conference on Machine Learning, ICML 2021, Proceedings of Machine Learning Research, Vol. 139, PMLR, Cambrige, pp. 10096–10106.
]Search in Google Scholar
[
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L. and Polosukhin, I. (2017). Attention is all you need, in I. Guyon et al. (Eds), Advances in Neural Information Processing Systems, Vol. 30, Curran Associates, Inc., Red Hook.
]Search in Google Scholar
[
Wu, Y., Dong, H., Grosse, R.B. and Ba, J. (2020). The scattering compositional learner: Discovering objects, attributes, relationships in analogical reasoning, CoRR: abs/2007.04212.
]Search in Google Scholar
[
Zhang, C., Gao, F., Jia, B., Zhu, Y. and Zhu, S.-C. (2019a). Raven: A dataset for relational and analogical visual reasoning, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA, pp. 5312–5322.
]Search in Google Scholar
[
Zhang, C., Jia, B., Gao, F., Zhu, Y., Lu, H. and Zhu, S.-C. (2019b). Learning perceptual inference by contrasting, in H. Wallach et al. (Eds), Advances in Neural Information Processing Systems, Vol. 32, Curran Associates, Inc., Red Hook.
]Search in Google Scholar
[
Zhuo, T. and Kankanhalli, M.S. (2021). Effective abstract reasoning with dual-contrast network, 9th International Conference on Learning Representations, ICLR 2021, (virtual).
]Search in Google Scholar
