L3A: Label-Augmented Analytic Adaptation for Multi-Label Class Incremental Learning
Authors: Xiang Zhang, Run He, Chen Jiao, Di Fang, Ming Li, Ziqian Zeng, Cen Chen, Huiping Zhuang
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on MS-COCO and PASCAL VOC datasets demonstrate that L3A outperforms existing methods in MLCIL tasks. Our code is available at https: //github.com/scut-zx/L3A. 4. Experiments |
| Researcher Affiliation | Academia | 1Shien-Ming Wu School of Intelligent Engineering, South China University of Technology, Guangzhou, China 2School of Future Technology, South China University of Technology, Guangzhou, China 3Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ). Correspondence to: Huiping Zhuang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 shows the pseudo-code of L3A, which utilizes the PL module to generate overall labels, extracts the sample features, and recursively updates the classifier by WAC. Algorithm 1 Training process of L3A |
| Open Source Code | Yes | Our code is available at https: //github.com/scut-zx/L3A. |
| Open Datasets | Yes | We follow previous works (Dong et al., 2023; De Min et al., 2024) in MLCIL and evaluate our method on MS-COCO 2014 (Lin et al., 2014) and PASCAL VOC 2007 (Everingham et al., 2010) datasets. |
| Dataset Splits | No | Let the phases as {D1, D2, , Dt, }, Dt is divided into a training set Dtrain t and a test set Dtest t . Dtrain t = {(X t,1, yt,1), , (X t,i, yt,i), , (X t,Nt, yt,Nt)} of size Nt, where X t,i represents a input samples tensor, yt,i represents the corresponding multi-hot labels vector. ...The cumulative label space for testing expands incrementally and is defined as C1:t = C1 Ct. (1) MS-COCO B0-C10: the model is trained across all 80 classes, divided into 8 continual learning phases, each learning 10 classes. |
| Hardware Specification | No | No specific hardware details (GPU models, CPU types, memory amounts) were explicitly mentioned for running the experiments. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow, CUDA) were explicitly mentioned in the paper. |
| Experiment Setup | Yes | The batch size is set to 64 for MS-COCO and 256 for PASCAL VOC. In all experimental protocols, we set the regularization term γ in Equation (8) to 1000, and the buffer layer size to 8192 for MS-COCO and PASCAL VOC. Table 5. The ablation study on regularization term (γ). Table 6. The ablation study on buffer layer size. Table 7. The ablation study on confidence threshold (η). |