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..
Revisiting Logistic-softmax Likelihood in Bayesian Meta-Learning for Few-Shot Classification
Authors: Tianjun Ke, Haoqun Cao, Zenan Ling, Feng Zhou
NeurIPS 2023 | Venue PDF | 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. |