Rectify Heterogeneous Models with Semantic Mapping

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Co-Transport for Class-Incremental Learning (2021, neutral): “model fusion [37], domain adaptation [7], object detection [10], model reuse [56, 57], and generative models [2, 4].”
Learning Placeholders for Open-Set Recognition (2021, background): “However, this closed-world assumption comes to an end when the test set includes unseen categories [16, 2, 42, 35].”
Few-Shot Learning With a Strong Teacher (2021, background): “[52], [53], [54] proposed special regularization strategies used during ﬁne-tuning.”
Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning (2024, background): “9, there are other ways to learn the mapping and complete the prototype matrix, e.g ., Linear Regression (LR) and Optimal Transport (OT) [33, 65].”
Storage Fit Learning with Feature Evolvable Streams (2020, background): “Over the last several years, feature evolvable learning has drawn extensive attentions (Zhang et al. 2016; Hou, Zhang, and Zhou 2017a; Hou and Zhou 2018; Ye et al. 2018; Zhang et al. 2020), where old features will vanish and new features will emerge when data streams come continuously or in an onlin”