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..
DIVA: Dataset Derivative of a Learning Task
Authors: Yonatan Dukler, Alessandro Achille, Giovanni Paolini, Avinash Ravichandran, Marzia Polito, Stefano Soatto
ICLR 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To illustrate the flexibility of DIVA, we report experiments on sample auto-curation tasks such as outlier rejection, dataset extension, and automatic aggregation of multi-modal data. |
| Researcher Affiliation | Collaboration | Yonatan Dukler1,2 , Alessandro Achille1, Giovanni Paolini1, Avinash Ravichandran1, Marzia Polito1, Stefano Soatto1 1 Amazon Web Services, EMAIL 2 Department of Mathematics, University of California, Los Angeles EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | No | The paper does not provide an explicit statement about the release of source code or a link to a code repository for the described methodology. |
| Open Datasets | Yes | For our experiments we use the CUB-200 (Welinder et al., 2010), FGVC-Aircraft, (Maji et al., 2013), Stanford Cars (Krause et al., 2013), Caltech-256 (Griffin et al., 2007), Oxford Flowers 102 (Nilsback & Zisserman, 2008), MIT-67 Indoor (Quattoni & Torralba, 2009), Street View House Number (Netzer et al., 2011), and the Oxford Pets (Parkhi et al., 2012) visual recognition and classification datasets. |
| Dataset Splits | Yes | For Ren et al. (2018), we set aside 20% of the training samples as validation for the reweight step, but use all samples for the final training (in parentheses). We can, of course, compute a validation loss using a separate validation set. However, as we will see in Section 3.3, we can also use a leave-one-out cross-validation loss directly on the training set, without any requirement of a separate validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using specific models and pre-trained networks but does not provide specific software dependencies with version numbers (e.g., programming language, libraries, or frameworks with their respective versions) required for replication. |
| Experiment Setup | Yes | In all experiments, we use the network as a fixed feature extractor, and train a linear classifier on top of the network features using the weighted L2 loss eq. (5) and optimize the weights using DIVA. ... We apply only 1-3 gradient optimization steps with a relatively large learning rate ' 0.1. This early stopping both regularizes the solution and decreases the wall-clock time required by the method. We initialize so that i = 1 for all samples. |