Rate My Professor Jit Yan

Jit Yan Lim's research specializations lie in few-shot learning, computer vision, and deep learning. He actively contributes to the academic community through peer-reviewed publications in leading journals such as Expert Systems with Applications, Neurocomputing, and IEEE Access. Among his key works are "LAP-GAN: Label augmentation with perceptual loss for self-supervised text-to-image synthesis," published on 15 January 2026 in Expert Systems with Applications (co-authored with Tan Y. X., Lim K. M., & Lee C. P.); "A review of few-shot fine-grained image classification," 25 May 2025 in Expert Systems with Applications (with Lim J. M., Lim K. M., & Lee C. P.); "A review of few-shot image classification: Approaches, datasets and research trends," 7 October 2025 in Neurocomputing (with Lim J. Y., Lim K. M., Lee C. P., & Tan Y. X.); "SFT: Few-Shot Learning via Self-Supervised Feature Fusion With Transformer," 18 June 2024 in IEEE Access (with Lim K. M., Lee C. P., & Tan Y. X.); and "SSL-ProtoNet: Self-supervised Learning Prototypical Networks for few-shot learning," 15 March 2024 in Expert Systems with Applications (with Lim K. M., Lee C. P., & Tan Y. X.). These publications highlight his expertise in self-supervised methods and comprehensive surveys of few-shot learning advancements. Furthermore, Lim engages in postgraduate supervision and serves as an AI Change Champion, guiding the incorporation of AI tools in IT curricula at Monash University Malaysia. He is also affiliated with the Medical AI Research Hub.