Addressing Challenges in Open World Recognition

In this research, we probe into the research works in Open World Recognition that share similar goal to our works in self-learning AI.

In-Domain and Out-of-Domain Separation in Open World Recognition

This is Gusti’s PhD work. In this work, we investigated an important aspect in Open World Recognition (OWR), i.e. the ability of an intelligent system to differentiate between in-domain and out-of-domain distributions. This ability will enable the system to collect appropriate in-domain unknown samples for continual learning in OWR.


  • Gusti Ahmad Fanshuri Alfarisy
  • Owais Ahmed Malik (main supervisor)
  • Ong Wee Hong



  • G. A. F. Alfarisy, O. A. Malik and O. W. Hong, “Quad-Channel Contrastive Prototype Networks for Open-Set Recognition in Domain-Specific Tasks,” in IEEE Access, vol. 11, pp. 48578-48592, 2023, doi: 10.1109/ACCESS.2023.3275743. (pdf)
  • G. Ahmad Fanshuri Alfarisy, O. Ahmed Malik and O. Wee Hong, “Evolutionary Simulated Annealing for Transfer Learning Optimization in Plant-Species Identification Domain,” 2022 IEEE 9th International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA), Chemnitz, Germany, 2022, pp. 1-6, doi: 10.1109/CIVEMSA53371.2022.9853679. (pdf)