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 [1].
Grounding inductive biases in natural images: invariance stems from variations in data
Authors: Diane Bouchacourt, Mark Ibrahim, Ari Morcos
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We train Res Net18 on Image Net and report results on the validation set as in commonly done (e.g. in Touvron et al. [33]) since the labelled test set is not publicly available. Fixed Size Center Crop corresponds to what is usually done for augmenting validation/test images, i.e. resize the image to 256 on the shorter dimension and take a center crop of size 224. |
| Researcher Affiliation | Industry | Diane Bouchacourt , Mark Ibrahim , Ari S. Morcos Facebook AI Research EMAIL |
| Pseudocode | No | No, the paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | Code to reproduce the main experiments is available at https://github.com/ facebookresearch/grounding-inductive-biases. |
| Open Datasets | Yes | We train Res Net18 on Image Net and report results on the validation set as in commonly done (e.g. in Touvron et al. [33]) since the labelled test set is not publicly available. |
| Dataset Splits | Yes | We train Res Net18 on Image Net and report results on the validation set as in commonly done (e.g. in Touvron et al. [33]) since the labelled test set is not publicly available. |
| Hardware Specification | Yes | All models were trained on 8 NVIDIA V100 GPUs for 100 epochs. |
| Software Dependencies | No | No, the paper mentions the use of the PyTorch library but does not specify its version number or any other software dependencies with specific versions. |
| Experiment Setup | Yes | We use a batch size of 256 and train our models for 100 epochs... We use Adam with a learning rate of 1e-3. Default values for the distribution over s are s = 0.08, s+ = 1. |