Not Just Object, But State: Compositional Incremental Learning without Forgetting
Authors: Yanyi Zhang, Binglin Qiu, Qi Jia, Yu Liu, Ran He
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on two datasets exhibit state-of-the-art performance achieved by Comp ILer. |
| Researcher Affiliation | Academia | Yanyi Zhang 1, Binglin Qiu 1, Qi Jia 1, Yu Liu 1, Ran He 2 1 International School of Information Science & Engineering, Dalian University of Technology 2 MAIS&CRIPAC, Institute of Automation, Chinese Academy of Sciences |
| Pseudocode | Yes | Algorithm 1: Training Procedure of Comp ILer for composition-IL |
| Open Source Code | Yes | Code and datasets are available at: https://github.com/Yanyi-Zhang/Comp ILer. |
| Open Datasets | Yes | Code and datasets are available at: https://github.com/Yanyi-Zhang/Comp ILer. As there are no existing datasets suitable for composition-IL, we re-organize the data in Clothing16K [46] and UT-Zappos50K [44], and construct two new datasets tailored for composition-IL, namely Split-Clothing and Split-UT-Zappos. |
| Dataset Splits | No | We allocate 80% of the dataset for training and the remaining 20% for testing. Consistent with Split-Clothing, we employ 80% of the images for training and 20% for testing. No explicit mention of a separate validation split percentage is provided. |
| Hardware Specification | No | The paper does not specify the hardware used for experiments, such as specific GPU or CPU models, memory, or cloud instance types. |
| Software Dependencies | No | The paper mentions using the Adam optimizer but does not specify any software dependencies (e.g., libraries, frameworks) with version numbers. |
| Experiment Setup | Yes | For multi-pool prompt learning, the size of each pool is set to 20, and each prompt has 5 tokens. We select top-5 prompts from each pool and generate a fused prompt. During training, we utilize the Adam optimizer [9] with a batch size of 16. The whole Comp ILer undergoes training for 25 epochs on the Split Clothing, for 10 epochs on the 5-task Split-UT-Zappos, and for 3 epochs on the 10-task Split-UT-Zappos. For the Split-Clothing and the 10-task Split-UT-Zappos, we set the learning rate to 0.03, while we use a learning rate of 0.02 for the 5-task Split-UT-Zappos. Note that, for all the methods, their results are averaged over three runs with the corresponding standard deviations reported to mitigate the influence of random factors. As there are a few hyper-parameters in the model, we conduct a rigorous tuning on them. For instance, we set θthre to π/2 for all settings. For Split-Clothing, the loss weights λ1 and λ3 are set to 0.1; λ2 is set to 10^−7; α and β for SCE loss are 0.006 and 0.3, and the parameter µ during inference is 0.5. For 5-task Split-UT-Zappos, λ1, λ2, λ3, α, β and µ are set to 1.0, 3 × 10^−6, 0.7, 0.01, 0.7 and 0.02, respectively. For 10-task Split-UT-Zappos, λ1, λ2, λ3, α, β and µ are set to 0.5, 10^−7, 0.1, 0.05, 0.4 and 0.03. |