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..
Attention Temperature Matters in ViT-Based Cross-Domain Few-Shot Learning
Authors: Yixiong Zou, Ran Ma, Yuhua Li, Ruixuan Li
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on four CDFSL datasets validate the rationale of our interpretation and method, showing we can consistently outperform state-of-the-art methods. |
| Researcher Affiliation | Academia | School of Computer Science and Technology, Huazhong University of Science and Technology EMAIL |
| Pseudocode | No | The paper describes methods in text and equations, but does not include any explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our codes are available at https://github.com/Zoilsen/Attn_Temp_CDFSL. |
| Open Datasets | Yes | Following current works [2, 12], we utilize the mini Image Net dataset [40] as the source dataset, and utilize 4 cross-domain datasets as the target datasets, including Crop Diseases [30], Euro SAT [13], ISIC2018 [5] and Chest X [44] for few-shot training and evaluation, using the k-way n-shot classification as stated in section 2.1. |
| Dataset Splits | Yes | During the learning and testing on DT , for the fair comparison, current works [2, 12] adopt a k-way n-shot paradigm to sample from DT to construct small datasets (i.e., episodes) consisting of k classes and n training samples in each class. Based on episodes, the model learns from these k n samples (a.k.a. support set, {xij, yi}k,n i=1,j=1) and is evaluated on testing samples from these k classes (a.a.k. query set, {xq}). |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or cloud computing instance specifications used for running the experiments. |
| Software Dependencies | No | The paper mentions the use of the Adam optimizer but does not specify version numbers for any software dependencies or libraries such as Python, PyTorch, or CUDA. |
| Experiment Setup | Yes | We use the Adam [16] optimizer with a learning rate of 0.001 for the classifier and 10 6 for the backbone network. During the target-domain few-shot evaluation, we set the temperature for the first two blocks as 0.3, and set the attention of the CLS token to 0 for blocks whose ID is greater than 4. |