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..
Generative vs. Discriminative: Rethinking The Meta-Continual Learning
Authors: Mohammadamin Banayeeanzade, Rasoul Mirzaiezadeh, Hosein Hasani, Mahdieh Soleymani
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | extensive experiments on standard benchmarks demonstrate the effectiveness of the proposed method. |
| Researcher Affiliation | Academia | Mohammadamin Banayeeanzade , Rasoul Mirzaiezadeh , Hosein Hasani , Mahdieh Soleymani Baghshah Department of Computer Engineering Sharif University of Technology EMAIL, EMAIL EMAIL, EMAIL |
| Pseudocode | No | The paper describes its methods using prose and mathematical formulations but does not include explicit pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code is publicly available at https://github.com/aminbana/Ge MCL. |
| Open Datasets | Yes | We have performed our experiments on Omniglot [27], Mini-Image Net [51], and CIFAR-100 [25] datasets. |
| Dataset Splits | Yes | We use 763 and 200 classes for meta-train and meta-validation respectively, and others for meta-test. ... Mini-Image Net... is divided into 64, 16, 20 classes for train, validation, and test meta-phases respectively. ... CIFAR-100 dataset... we use 70 and 30 classes for meta-train and meta-test phases respectively. |
| Hardware Specification | No | The paper does not explicitly mention any specific hardware details such as GPU models, CPU models, or memory specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., Python, PyTorch, CUDA versions) used for the experiments. |
| Experiment Setup | Yes | The training is done with a learning rate of 0.001, decaying to half every 0.1 of the training length. ... For this dataset, we used 20-way 10-shot and 20-way 30-shot train and validation episodes respectively with 30 query samples for both. |