Academic Research#
My research moslty focuses on continual learning, but my interests span other fields of deep learning such as learning with noisy data, satellite and remote‑sensing data, and self‑supervised learning.
All my papers involving Continual Learning are available via the Mammoth library, a PyTorch library for continual learning available on GitHub at github.com/aimagelab/mammoth.
A Second-Order Perspective on Model Compositionality and Incremental Learning Task Arithmetic ICLR
May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels Continual Learning NeurIPS
CLIP with Generative Latent Replay: a Strong Baseline for Incremental Learning Continual Learning NeurIPS
Semantic Residual Prompts for Continual Learning Continual Learning NeurIPS
On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning Continual Learning NeurIPS
Transfer without Forgetting Continual Learning ECCV
Class-Incremental Continual Learning into the eXtended DER-verse Continual Learning IEEE TPAMI
Continual semi-supervised learning through contrastive interpolation consistency Continual Learning PRL
Spotting Virus From Satellites: Modeling the Circulation of West Nile Virus Through Graph Neural Networks Remote Sensing IEEE TGRS
Training Convolutional Neural Networks to Score Pneumonia in Slaughtered Pigs Semantic Segmentation MDPI Animals