Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification
Authors: Massimiliano Patacchiola, John Bronskill, Aliaksandra Shysheya, Katja Hofmann, Sebastian Nowozin, Richard Turner
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we report on experiments on VTAB+MD (Dumoulin et al., 2021) and ORBIT (Massiceti et al., 2021). |
| Researcher Affiliation | Collaboration | Massimiliano Patacchiola University of Cambridge mp2008@cam.ac.uk John Bronskill University of Cambridge jfb54@cam.ac.uk Aliaksandra Shysheya University of Cambridge as2975@cam.ac.uk Katja Hofmann Microsoft Research kahofman@microsoft.com Sebastian Nowozin nowozin@gmail.com Richard E. Turner University of Cambridge ret26@cam.ac.uk |
| Pseudocode | Yes | The pseudo-code for train and test is provided in Appendix B. |
| Open Source Code | Yes | The code is released with an open-source license 1. 1https://github.com/mpatacchiola/contextual-squeeze-and-excitation |
| Open Datasets | Yes | In this section we report on experiments on VTAB+MD (Dumoulin et al., 2021) and ORBIT (Massiceti et al., 2021). |
| Dataset Splits | Yes | MD test results are averaged over 1200 tasks per-dataset (confidence intervals in appendix). We did not use data augmentation. |
| Hardware Specification | Yes | We used three workstations (CPU 6 cores, 110GB of RAM, and a Tesla V100 GPU) |
| Software Dependencies | No | The paper mentions 'Efficient Net B0 from the official Torchvision repository' and 'Adam optimizer' but does not specify version numbers for general software dependencies like Python or PyTorch. |
| Experiment Setup | Yes | We used the meta-training protocol of Bronskill et al. (2021) (10K training tasks, updates every 16 tasks), the Adam optimizer with a linearly-decayed learning rate in [10 3, 10 5] for both the Ca SE and linear-head. The head is updated 500 times using a random mini-batch of size 128. |