  In this paper, we introduce our approach to the 5th CLVision Challenge, which
presents distinctive challenges beyond traditional class incremental learning.
Unlike standard settings, this competition features the recurrence of
previously encountered classes and includes unlabeled data that may contain
Out-of-Distribution (OOD) categories. Our approach is based on Winning
Subnetworks to allocate independent parameter spaces for each task addressing
the catastrophic forgetting problem in class incremental learning and employ
three training strategies: supervised classification learning, unsupervised
contrastive learning, and pseudo-label classification learning to fully utilize
the information in both labeled and unlabeled data, enhancing the
classification performance of each subnetwork. Furthermore, during the
inference stage, we have devised an interaction strategy between subnetworks,
where the prediction for a specific class of a particular sample is the average
logits across different subnetworks corresponding to that class, leveraging the
knowledge learned from different subnetworks on recurring classes to improve
classification accuracy. These strategies can be simultaneously applied to the
three scenarios of the competition, effectively solving the difficulties in the
competition scenarios. Experimentally, our method ranks first in both the
pre-selection and final evaluation stages, with an average accuracy of 0.4535
during the preselection stage and an average accuracy of 0.4805 during the
final evaluation stage.
