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..
Weak-shot Fine-grained Classification via Similarity Transfer
Authors: Junjie Chen, Li Niu, Liu Liu, Liqing Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comprehensive experiments demonstrate the effectiveness of our weak-shot setting and our Sim Trans method. Datasets and codes are available at https://github.com/bcmi/Sim Trans-Weak-Shot-Classification. |
| Researcher Affiliation | Academia | Junjie Chen, Li Niu , Liu Liu, Liqing Zhang Mo E Key Lab of Artificial Intelligence, Department of Computer Science and Engineering, Shanghai Jiao Tong University EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods in prose and with diagrams but does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | Datasets and codes are available at https://github.com/bcmi/Sim Trans-Weak-Shot-Classification. |
| Open Datasets | Yes | We conduct experiments based on three fine-grained datasets: Comp Cars [52] (Car for short), CUB [48], and FGVC [26]. |
| Dataset Splits | No | Table 1 provides 'Train' and 'Test' statistics for the datasets, and the text mentions 'base training/test set' and 'novel training set'. However, there is no explicit separate 'validation' split with specific counts or percentages provided for the datasets, other than mentioning 'cross-validation' for hyperparameter tuning. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions using ResNet50 as a backbone but does not specify any software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow versions, CUDA versions). |
| Experiment Setup | Yes | The classification loss and the adversarial loss are balanced with a hyper-parameter β, set as 0.1 via cross-validation. ... where α is a hyper-parameter set as 0.1 by cross-validation. ... We use Cm = 10 and M = 100 for both training and testing of Sim Net. |