Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
Authors: Tianjun Ke, Haoqun Cao, Zenan Ling, Feng Zhou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we present the results for few-shot classification tasks including accuracy and uncertainty quantification. We consider two challenging standard benchmark datasets: the Caltech UCSD Birds [32] and mini-Image Net [24]. |
| Researcher Affiliation | Academia | Center for Applied Statistics and School of Statistics, Renmin University of China School of EIC, Huazhong University of Science and Technology |
| Pseudocode | Yes | The complete training and test procedure is described in Alg. 1 of Appendix VII. |
| Open Source Code | Yes | Code is publicly available at https://github.com/keanson/revisit-logistic-softmax. |
| Open Datasets | Yes | We consider two challenging standard benchmark datasets: the Caltech UCSD Birds [32] and mini-Image Net [24]. |
| Dataset Splits | Yes | Following the procedure of prior work [20], we employ a standard Conv4 architecture [31] as the backbone and assess our models under six different settings, including 1-shot and 5-shot scenarios for both in-domain and cross-domain tasks. ... we first tune the temperature parameter on the validation set for alignment and then compute the ECE and MCE on the test set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, memory amounts, or detailed computer specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | We train and evaluate our models (denoted as CDKT) with the ELBO loss (denoted as ML) using the default number of epochs from Patacchiola et al. [20], and with the predictive likelihood loss (denoted as PL) using 800 epochs. However, we find that the ML (τ < 1) domain-transfer experiment requires 800 epochs to avoid underfitting. We set the temperature parameter to τ = 0.5 for the 5-shot PL experiment of mini-Image Net and domain transfer, and τ = 0.2 for all other experiments. |