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..
Test-Time Amendment with a Coarse Classifier for Fine-Grained Classification
Authors: Kanishk Jain, Shyamgopal Karthik, Vineet Gandhi
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our approach on the i Naturalist-19 [40] and tiered Image Net-H [41] datasets. We conduct five runs for each experiment in tables 1 and 2 and report the mean and standard deviations. |
| Researcher Affiliation | Academia | Kanishk Jain1, Shyamgopal Karthik2, Vineet Gandhi1 1IIIT Hyderabad 2University of Tübingen |
| Pseudocode | No | The paper does not contain a clearly labeled "Pseudocode" or "Algorithm" block, nor does it present structured steps in a code-like format. |
| Open Source Code | Yes | The code is available at: https://github.com/kanji95/Hierarchical-Ensembles |
| Open Datasets | Yes | Like prior works [6, 8, 7], we evaluate our approach on the i Naturalist-19 [40] and tiered Image Net-H [41] datasets. |
| Dataset Splits | No | The paper mentions evaluation metrics for "validation" but does not explicitly provide details about a dedicated validation dataset split (e.g., percentages or sample counts) distinct from the training and test sets. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU models, or detailed computer specifications used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library names with version numbers (e.g., Python, PyTorch, or TensorFlow versions). |
| Experiment Setup | Yes | For the i Naturalist-19 dataset, we initialize the Res Net-18 weights with a pre-trained Image Net model and train the classifiers using a customized SGD optimizer for 100 epochs, with different learning rates for the backbone network and the fully connected layer (0.01 and 0.1, respectively). For the tiered Image Net-H dataset, we train the Res Net-18 from scratch (since it is derived from the Image Net dataset) and use the AMSGrad variant of the Adam optimizer with a learning rate of 1e 5 for 120 epochs. |