---
title: "google-deepmind/dsprites-dataset: papers citing it"
description: "13 papers in the tracked journals link to the GitHub repository google-deepmind/dsprites-dataset in their Crossref records, the first in 2019; most of them (3) in 2025."
url: https://scholar4ai.com/code/github-google-deepmind-dsprites-dataset
markdown_url: https://scholar4ai.com/code/github-google-deepmind-dsprites-dataset.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "13 papers in the tracked journals link to the GitHub repository google-deepmind/dsprites-dataset in their Crossref records, the first in 2019; most of them (3) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# google-deepmind/dsprites-dataset: papers linking to it

Research papers in the 480 tracked journals that link to google-deepmind/dsprites-dataset (https://github.com/google-deepmind/dsprites-dataset), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- GitHub
- 541 stars
- 69 forks
- Jupyter Notebook
- license Apache-2.0
- created 2017
- https://github.com/google-deepmind/dsprites-dataset

All repositories: https://scholar4ai.com/code.md

## Key findings

- 13 papers in the tracked journals link to the GitHub repository google-deepmind/dsprites-dataset in their Crossref records, the first in 2019; most of them (3) in 2025.
- By category: AI (13); the journals with the most are Machine Learning (3), International Journal of Computer Vision (3) and Pattern Recognition (2).

## By year

*Papers linking to google-deepmind/dsprites-dataset by publication year*

| Year | Papers |
|---|---:|
| 2019 | 1 |
| 2020 | 1 |
| 2022 | 3 |
| 2023 | 1 |
| 2024 | 3 |
| 2025 | 3 |
| 2026 | 1 |

## Journals

*Journals whose papers link to google-deepmind/dsprites-dataset most*

| Journal | Papers |
|---|---:|
| [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | 3 |
| [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | 3 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 2 |
| [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | 1 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 1 |

## Papers

*Papers linking to google-deepmind/dsprites-dataset, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2019-10-23 | [Augmented Autoencoders: Implicit 3D Orientation Learning for 6D Object Detection](https://doi.org/10.1007/s11263-019-01243-8) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Martin Sundermeyer, Zoltán-Csaba Márton, Maximilian Durner et al. | 139 | cites it |
| 2023-11-29 | [Explainable reinforcement learning (XRL): a systematic literature review and taxonomy](https://doi.org/10.1007/s10994-023-06479-7) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Yanzhe Bekkemoen | 54 | cites it |
| 2020-08-07 | [Attribute-based regularization of latent spaces for variational auto-encoders](https://doi.org/10.1007/s00521-020-05270-2) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ashis Kumar Pati, Alexander Lerch | 44 | cites it |
| 2026-06-01 | [Parameter-Efficient Fine-Tuning for Pre-Trained Vision Models: A Survey and Benchmark](https://doi.org/10.1007/s11263-026-02864-6) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Yi Xin, Jianjiang Yang, Siqi Luo et al. | 28 | cites it |
| 2022-07-21 | [SSDMM-VAE: variational multi-modal disentangled representation learning](https://doi.org/10.1007/s10489-022-03936-z) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Arnab Kumar Mondal, Ajay Sailopal, Parag Singla et al. | 8 | cites it |
| 2024-04-30 | [Improving disentanglement in variational auto-encoders via feature imbalance-informed dimension weighting](https://doi.org/10.1016/j.knosys.2024.111818) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Yue Liu, Zhenyao Yu, Zitu Liu et al. | 7 | cites it |
| 2025-02-06 | [Discovering fully semantic representations via centroid- and orientation-aware feature learning](https://doi.org/10.1038/s42256-024-00978-5) | [Nature Machine Intelligence](https://scholar4ai.com/journals/nature-machine-intelligence.md) | Jaehoon Cha, Jinhae Park, Samuel Pinilla et al. | 6 | cites it |
| 2024-01-06 | [Benchmarking and Analysis of Unsupervised Object Segmentation from Real-World Single Images](https://doi.org/10.1007/s11263-023-01973-w) | [International Journal of Computer Vision](https://scholar4ai.com/journals/international-journal-of-computer-vision.md) | Yafei Yang, Bo Yang | 3 | cites it |
| 2024-08-30 | [A Novel Multimodal Generative Learning Model based on Basic Fuzzy Concepts](https://doi.org/10.1007/s12559-024-10336-7) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Huankun Sheng, Hongwei Mo, Tengteng Zhang | 2 | cites it |
| 2025-10-24 | [DualAdaptNet: Enhancing domain adaptation regression with error accumulation reduction and dual-head heterogeneous learning](https://doi.org/10.1016/j.patcog.2025.112641) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Xin Wang, Yuan Wu, Xingyu Zhu et al. | 2 | cites it |
| 2025-08-19 | [Toward practical human-interpretable explanations](https://doi.org/10.1007/s10994-025-06852-8) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Alon Malach, Amiel Meiseles, Ron Bitton et al. | 1 | cites it |
| 2022-03-29 | [DEFT: distilling entangled factors by preventing information diffusion](https://doi.org/10.1007/s10994-022-06134-7) | [Machine Learning](https://scholar4ai.com/journals/machine-learning.md) | Jiantao, Lin Wang, Bo Yang et al. | 0 | cites it |
| 2022-09-22 | [Disentangling the correlated continuous and discrete generative factors of data](https://doi.org/10.1016/j.patcog.2022.109055) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Jaewoong Choi, Geonho Hwang, Myungjoo Kang | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). google-deepmind/dsprites-dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/github-google-deepmind-dsprites-dataset

```bibtex
@misc{scholar4ai-code-github-google-deepmind-dsprites-dataset,
  author = {{Scholar4AI}},
  title = {google-deepmind/dsprites-dataset: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/github-google-deepmind-dsprites-dataset}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).