@InProceedings{	  icpr2024,
  author	= {Munsif Ali AND Leonardo Rossi AND Massimo Bertozzi},
  title		= "{CFTS-GAN: Continual Few-Shot Teacher Student for
		  Generative Adversarial Networks}",
  editor	= "Antonacopoulos, Apostolos and Chaudhuri, Subhasis and
		  Chellappa, Rama and Liu, Cheng-Lin and Bhattacharya, Saumik
		  and Pal, Umapada",
  booktitle	= "Procs.~Intl.~Conf.~on Pattern Recognition -- ICPR 2024",
  series	= "Lecture Notes in Computer Science",
  volume	= "15325",
  year		= "2025",
  publisher	= "Springer Nature Switzerland",
  address	= "Cham",
  pages		= "249--262",
  abstract	= "Few-shot and continual learning face two well-known
		  challenges in GANs: overfitting and catastrophic
		  forgetting. Learning new tasks results in catastrophic
		  forgetting in deep learning models. In the case of a
		  few-shot setting, the model learns from a very limited
		  number of samples (e.g. 10 samples), which can lead to
		  overfitting and mode collapse. So, this paper proposes a
		  Continual Few-shot Teacher-Student technique for the
		  generative adversarial network (CFTS-GAN) that considers
		  both challenges together. Our CFTS-GAN uses an adapter
		  module as a student to learn a new task without affecting
		  the previous knowledge. To make the student model efficient
		  in learning new tasks, the knowledge from a teacher model
		  is distilled to the student. In addition, the Cross-Domain
		  Correspondence (CDC) loss is used by both teacher and
		  student to promote diversity and to avoid mode collapse.
		  Moreover, an effective strategy of freezing the
		  discriminator is also utilized for enhancing performance.
		  Qualitative and quantitative results demonstrate more
		  diverse image synthesis and produce qualitative samples
		  comparatively good to very stronger state-of-the-art
		  models.",
  eprint	= "doi:10.1007/978-3-031-78389-0_17",
  isbn		= "978-3-031-78389-0"
}

@InProceedings{	  icpr2024-control,
  author	= "Alex Ergasti AND Claudio Ferrari AND Tomaso Fontanini AND
		  Massimo Bertozzi AND Andrea Prati",
  editor	= "Palaiahnakote, Shivakumara and Schuckers, Stephanie and
		  Ogier, Jean-Marc and Bhattacharya, Prabir and Pal, Umapada
		  and Bhattacharya, Saumik",
  title		= "{Controllable Face Synthesis with Semantic Latent
		  Diffusion Models}",
  booktitle	= "Pattern Recognition. ICPR 2024 International Workshops and
		  Challenges",
  year		= "2025",
  publisher	= "Springer Nature Switzerland",
  address	= "Cham",
  pages		= "337--352",
  doi		= "doi:10.1007/978-3-031-87660-8_25",
  eprint	= "doi:10.1007/978-3-031-87660-8_25",
  series	= "Lecture Notes in Computer Science",
  volume	= "15615",
  isbn		= "978-3-031-87660-8"
}