1. Týžden
	

	Štúdium doposial vybraných materiálov
	22.2. Stretnutie so školitelkou - Diskusia o cieloch práce a nedostatkoch predošlého modelu(napr. imbalance v datasete), možné riešenia problémov a teórie, plán práce na další týžden
	

	2. Týžden
	

	29.2. Stretnutie so školitelkou - Diskusia o nových materiáloch, plán práce na další týžden, prístup k projektu na ktorom staviame, odporúcania pre použitie nástrojov a knižníc
	Vytvorenie prostredia a rozbehanie modelu pre rozpoznávanie emócii 
	

	3. Týžden
	

	Rozbehanie projektu v google colab, príprava testovacích výpisov, parametrov pocas tréningu, export dát
	

	4. Týžden
	

	15.3. Online stretnutie so školitelkou - plán práce na další týžden, hladanie menšieho modelu pre úcely testovania, experimenty so sietou, refaktor kódu
	
	

	6. Týžden
	

	27.3.Stretnutie so školitelkou - riešenie problému s originálnou implementáciou miešania datasetu, stratifikovanie 
	
	

	7. Týžden
	

	2.4. Implementácia plnohodnotného miešania datasetu(predtým bolo ciastkové), implementácia stratifikácie 
	
	

	8. Týžden
	

	12.4. Online call so školitelkou - konzultácia výsledkov doterajších experimentov, dohodnutie dalšieho postupu 
	
	

	10. Týžden
	

	23.4. Stretnutie so školitelkou - feedback k text bc  
	
	

    Li, S. and Deng, W. 2022. "Deep Facial Expression Recognition: A Survey,"
    in IEEE Transactions on Affective Computing, vol. 13, no. 3, pp. 1195-1215.
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    Mollahosseini, A., Hasani, B. and Mahoor, M.H., 2017. Affectnet:
    A database for facial expression, valence, and arousal computing in the wild.
    IEEE Transactions on Affective Computing, 10(1), pp.18-31.
    

    Cao, Q., Shen, L., Xie, W., Parkhi, O.M. and Zisserman, A., 2018, May.
    Vggface2: A dataset for recognising faces across pose and age. In 2018 13th
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    Barros, P., Barakova, E., & Wermter, S. (2018). A deep neural model of emotion appraisal.
    arXiv preprint arXiv:1808.00252.
    

    Herashchenko, D., & Farkaš, I. (2023, December). Appearance-based gaze estimation enhanced with synthetic images using deep neural networks.
    In 2023 IEEE Symposium Series on Computational Intelligence (SSCI) (pp. 129-134). IEEE.
    

    Belanec, R., Lacko, P., & Malinovská, K. (2023, September). Controlling the Output of a Generative Model by Latent Feature Vector Shifting.
    In 2023 World Symposium on Digital Intelligence for Systems and Machines (DISA) (pp. 24-30). IEEE.
    

    Churamani, N., Kerzel, M., Strahl, E., Barros, P., & Wermter, S. (2017, May). Teaching emotion expressions to a human companion robot using deep neural architectures.
    In 2017 international joint conference on neural networks (IJCNN) (pp. 627-634). IEEE.
    

    Cao, Q., Shen, L., Xie, W., Parkhi, O. M., & Zisserman, A. (2018, May). Vggface2: A dataset for recognising faces across pose and age. In 2018 13th IEEE international
    conference on automatic face & gesture recognition (FG 2018) (pp. 67-74). IEEE.
    

    Kvasnicka, V., Benušková, L., Pospíchal, J., Farkaš, I., Tino, P., & Král, A. (1997). Úvod do teórie neurónových sietí. Iris.
    

    Liu, N., Wang, J., & Gong, Y. (2015, July). Deep self-organizing map for visual classification. In 2015 international joint conference on neural networks (IJCNN) (pp. 1-6). IEEE.
    

    Zhuang, F., Qi, Z., Duan, K., Xi, D., Zhu, Y., Zhu, H., ... & He, Q. (2020). A comprehensive survey on transfer learning. Proceedings of the IEEE, 109(1), 43-76.
    

    Aly, S., & Almotairi, S. (2020). Deep convolutional self-organizing map network for robust handwritten digit recognition. IEEE Access, 8, 107035-107045.
    

    Campesato, O. (2020). Artificial intelligence, machine learning, and deep learning. Mercury Learning and Information.
    

    Yao, A., Cai, D., Hu, P., Wang, S., Sha, L., & Chen, Y. (2016, October). HoloNet: towards robust emotion recognition in the wild. In Proceedings of the 18th ACM
    international conference on multimodal interaction (pp. 472-478).
	

    Hu, P., Cai, D., Wang, S., Yao, A., & Chen, Y. (2017, November). Learning supervised scoring ensemble for emotion recognition in the wild. In Proceedings of the
    19th ACM international conference on multimodal interaction (pp. 553-560).
    

    Rifai, S., Bengio, Y., Courville, A., Vincent, P., & Mirza, M. (2012). Disentangling factors of variation for facial expression recognition. In Computer
    Vision–ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part VI 12 (pp. 808-822). Springer Berlin Heidelberg.
    

    Liu, P., Han, S., Meng, Z., & Tong, Y. (2014). Facial expression recognition via a boosted deep belief network. In Proceedings of the IEEE conference on computer vision
    and pattern recognition (pp. 1805-1812).
    

    Zheng, C., Mendieta, M., & Chen, C. (2023). Poster: A pyramid cross-fusion transformer network for facial expression recognition. In Proceedings of the IEEE/CVF International
    Conference on Computer Vision (pp. 3146-3155).
	

    Mao, J., Xu, R., Yin, X., Chang, Y., Nie, B., & Huang, A. POSTER V2: A simpler and stronger facial expression recognition network. arXiv 2023. arXiv preprint arXiv:2301.12149.
    

    Zhang, S., Zhang, Y., Zhang, Y., Wang, Y., & Song, Z. (2023). A Dual-Direction Attention Mixed Feature Network for Facial Expression Recognition. Electronics, 12(17), 3595.
    

    Panagiotis, A., Filntisis, P. P., & Maragos, P. Exploiting emotional dependencies with graph convolutional networks for facial expression recognition. arXiv 2021. arXiv preprint arXiv:2106.03487.    
    

    Wen, Z., Lin, W., Wang, T., & Xu, G. (2023). Distract your attention: Multi-head cross attention network for facial expression recognition. Biomimetics, 8(2), 199.
    

https://github.com/knowledgetechnologyuhh/EmotionRecognitionBarros
https://github.com/Wicwik/DP
https://github.com/1980x/SCAN-CCI-FER/blob/master/main_affectnet.py
https://gitlab.matfyz.sk/
https://docs.python.org/3/library/pickle.html
https://cogsci.fmph.uniba.sk/~rebrova/fop-project/
https://docs.anaconda.com/free/miniconda/index.html
https://www.docker.com/