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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
UniCLIP: Unified Framework for Contrastive Language-Image Pre-training
Authors: Janghyeon Lee, Jongsuk Kim, Hyounguk Shon, Bumsoo Kim, Seung Hwan Kim, Honglak Lee, Junmo Kim
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,919 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,707 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, we show that each component that comprises Uni CLIP contributes well to the final performance. |
| Researcher Affiliation | Collaboration | Janghyeon Lee LG AI Research EMAIL Jongsuk Kim KAIST EMAIL |
| Pseudocode | No | The paper describes methods using text and mathematical formulas but does not include any explicitly labeled 'Pseudocode' or 'Algorithm' blocks. |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] |
| Open Datasets | Yes | Datasets For reproducibility, we use publicly available datasets for training and evaluation in our experiments, including CC3M [23], CC12M [2], De CLIP YFCC15M [13, 25] for training and Pets [18], CIFAR-10, CIFAR-100 [12], SUN397 [29], Food-101 [1], Flowers [17], Cars [11], Caltech101 [8], Aircraft [14], DTD [6], Image Net-1k [21], Flickr30k [19], COCO Captions [5] for evaluation. |
| Dataset Splits | Yes | Did you specify all the training details (e.g., data splits, hyperparameters, how they were chosen)? [Yes] |
| Hardware Specification | No | The paper's main text does not specify specific hardware details such as GPU/CPU models or types of internal clusters/cloud providers used for experiments, although the ethics statement indicates this information was provided. |
| Software Dependencies | No | The paper does not provide specific version numbers for ancillary software dependencies (e.g., 'Python 3.8, PyTorch 1.9'). |
| Experiment Setup | Yes | Settings For each original image and corresponding text caption, one weakly augmented image, two strongly augmented images, and one text form a positive group in our experiments. Detailed augmentation and optimization configurations can be found in Appendix. |