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..
Comparison Knowledge Translation for Generalizable Image Classification
Authors: Zunlei Feng, Tian Qiu, Sai Wu, Xiaotuan Jin, Zengliang He, Mingli Song, Huiqiong Wang
IJCAI 2022 | Venue PDF | 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. |