Comparison Knowledge Translation for Generalizable Image Classification
Authors: Zunlei Feng, Tian Qiu, Sai Wu, Xiaotuan Jin, Zengliang He, Mingli Song, Huiqiong Wang
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Exhaustive experiments show that CCT-Net achieves surprising generalization ability on unseen categories and SOTA performance on target categories. In the experiments, we adopt five datasets, including MNIST [Le Cun et al., 1998], CIFAR-10 [Krizhevsky, 2009], STL-10 [Adam et al., 2011], Oxford-IIIT Pets [Parkhi et al., 2012], and mini-Image Net [Jia et al., 2009], to verify the effectiveness of the proposed CKT-Task and CCT-Net. Table 1: The comparison with SOTA methods. |
| Researcher Affiliation | Collaboration | 1Zhejiang University 2Ningbo Research Institute, Zhejiang University 3Hangzhou Honghua Digital Technology Co., Ltd. 4Shanghai Institute for Advanced Study of Zhejiang University 5Alibaba-Zhejiang University Joint Research Institute of Frontier Technologies |
| Pseudocode | Yes | The complete training algorithm for CCT-Net is summarized in Algorithm 1&2 of the supplements. |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing the code or a link to a code repository. |
| Open Datasets | Yes | In the experiments, we adopt five datasets, including MNIST [Le Cun et al., 1998], CIFAR-10 [Krizhevsky, 2009], STL-10 [Adam et al., 2011], Oxford-IIIT Pets [Parkhi et al., 2012], and mini-Image Net [Jia et al., 2009], to verify the effectiveness of the proposed CKT-Task and CCT-Net. |
| Dataset Splits | No | The paper mentions that 'categories of each dataset are evenly split into the source and target categories' and '80% of the target datasets are used as annotated samples for semi-supervised methods'. However, it does not provide specific train/validation/test percentages or sample counts for reproduction, nor does it reference predefined splits with citations for all datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions) that would be needed to replicate the experiment. |
| Experiment Setup | No | The paper describes network architecture details such as 'Vi T-B/16 is adopted as the backbone', '12 attention heads', and 'fully connected layer: 4096, Leaky Re LU, linear layer: 1024, linear layer: 256'. However, it does not provide specific hyperparameter values like learning rate, batch size, or number of epochs in the main text. |