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..
Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition
Authors: Satoshi Tsutsui, Yanwei Fu, David Crandall
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The model is trained in an end-to-end manner, and our experiments demonstrate consistent improvement over baselines on one-shot fine-grained image classification benchmarks. |
| Researcher Affiliation | Academia | Satoshi Tsutsui Indiana University USA EMAIL Yanwei Fu Fudan University China EMAIL David Crandall Indiana University USA EMAIL |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Further implementation details are available as supplemental source code.2 http://vision.soic.indiana.edu/metairnet/ |
| Open Datasets | Yes | We use the fine-grained classification dataset of Caltech UCSD Birds (CUB) [32] for our main experiments, and another fine-grained dataset of North American Birds (NAB) [30] for secondary experiments. |
| Dataset Splits | Yes | For CUB, we use the same train/val/test split used in previous work [4], and for NAB we randomly split with a proportion of train:val:test = 2:1:1; see supplementary material for details. |
| Hardware Specification | No | The paper mentions 'an NVidia Titan Xp GPU' for a specific generation step during a pilot study (Section 3), but does not specify the hardware used for the main Meta IRNet experiments described in Section 5.1. |
| Software Dependencies | No | The paper mentions using 'Adam' optimizer and 'Res Net18' for image classification, but does not provide specific software dependencies with version numbers (e.g., Python, PyTorch/TensorFlow, CUDA versions). |
| Experiment Setup | Yes | We set λp = 0.1 and λz = 0.1, and perform 500 gradient descent updates with the Adam [18] optimizer with learning rate 0.01 for z and 0.0005 for the fully connected layers, to produce scale and shift parameters of the batch normalization layers. We train F and C with Adam with a default learning rate of 0.001. |