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.

我们介绍了应对第五届 CLVision 挑战赛的方法，该赛事存在传统类别增量学习之外的独特挑战，包括先前遇到的类别的循环出现以及可能包含未标记数据的分布外类别。我们的方法基于赢得子网络，为每个任务分配独立的参数空间以解决类别增量学习中的灾难性遗忘问题，并采用三种训练策略：有监督分类学习、无监督对比学习和伪标签分类学习，充分利用标记和未标记数据的信息，提高每个子网络的分类性能。此外，在推断阶段，我们设计了子网络之间的交互策略，对于特定样本的特定类别的预测是对应于该类别的不同子网络之间的平均 logits，从而利用不同子网络对循环出现的类别学习的知识来提高分类准确性。这些策略可以同时应用于比赛的三个场景，有效解决比赛场景中的困难。实验结果表明，我们的方法在预选阶段和最终评估阶段均排名第一，在预选阶段的平均准确率为 0.4535，在最终评估阶段的平均准确率为 0.4805。

2024 年第五届 CLVISION 挑战赛冠军解决方案

The Championship-Winning Solution for the 5th CLVISION Challenge 2024

Continual learning (CL) provides a framework for training models in
ever-evolving environments. Although re-occurrence of previously seen objects
or tasks is common in real-world problems, the concept of repetition in the
data stream is not often considered in standard benchmarks for CL. Unlike with
the rehearsal mechanism in buffer-based strategies, where sample repetition is
controlled by the strategy, repetition in the data stream naturally stems from
the environment. This report provides a summary of the CLVision challenge at
CVPR 2023, which focused on the topic of repetition in class-incremental
learning. The report initially outlines the challenge objective and then
describes three solutions proposed by finalist teams that aim to effectively
exploit the repetition in the stream to learn continually. The experimental
results from the challenge highlight the effectiveness of ensemble-based
solutions that employ multiple versions of similar modules, each trained on
different but overlapping subsets of classes. This report underscores the
transformative potential of taking a different perspective in CL by employing
repetition in the data stream to foster innovative strategy design.

这篇研究报告总结了 CVPR 2023 年的 CLVision 挑战赛，重点讨论了类增量学习中的数据重复问题，介绍了三个解决方案，实验证明基于模块集成的多版本策略对于利用数据流中的重复性进行持续学习是有效的。该报告强调了通过在数据流中利用重复性来促进创新策略设计的转变潜力。