Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Specifying What You Know or Not for Multi-Label Class-Incremental Learning
Authors: Aoting Zhang, Dongbao Yang, Chang Liu, Xiaopeng Hong, Yu Zhou
AAAI 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments validate that our method effectively alleviates catastrophic forgetting in MLCIL, surpassing the previous stateof-the-art by 3.3% on average accuracy for MS-COCO B0C10 setting without replay buffers. |
| Researcher Affiliation | Academia | 1Institute of Information Engineering, Chinese Academy of Sciences 2VCIP & TMCC & DISSec, College of Computer Science, Nankai University 3School of Cyber Security, University of Chinese Academy of Sciences 4Harbin Institute of Technology 5Tsinghua University EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper describes the proposed method, HCP, using prose and mathematical formulas, but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our codes are available at https://github.com/InfLoop111/HCP. |
| Open Datasets | Yes | HCP is evaluated on MS-COCO 2014 (Lin et al. 2014) and PASCAL VOC 2007 (Everingham 2007) datasets. |
| Dataset Splits | Yes | MS-COCO contains 82,081 training images and 40,137 test images, which covers 80 common objects with an average of 2.9 labels per image. PASCAL VOC contains 5,011 images in the train-val set, and 4,952 images in the test set. |
| Hardware Specification | No | The paper describes training parameters and model architecture but does not specify the hardware (e.g., GPU, CPU models) used for running experiments. |
| Software Dependencies | No | The paper mentions using Adam optimizer and One Cycle LR scheduler, but does not provide specific software versions for libraries, frameworks, or programming languages. |
| Experiment Setup | Yes | We train the model with a batch size of 64 for 20 epochs, using Adam (Kingma and Ba 2014) optimizer and One Cycle LR scheduler with a weight decay of 1e-4. In the base session, we set the learning rate to 4e-5. In the following sessions, it adjusts to 1e-4 for MS-COCO and 4e-5 for VOC. In dynamic feature purification module, we set 3 attention blocks for VOC and 1 for MS-COCO. |