https://arxiv.org/abs/2308.04052

Skip to main content
Cornell University
We gratefully acknowledge support from the Simons Foundation, member
institutions, and all contributors. Donate
 
arxiv logo > cs > arXiv:2308.04052
[                    ]

Help | Advanced Search

[All fields        ]
Search
arXiv logo
Cornell University Logo
[                    ] GO
quick links

  * Login
  * Help Pages
  * About

Computer Science > Machine Learning

arXiv:2308.04052 (cs)
[Submitted on 8 Aug 2023]

Title:The Five-Dollar Model: Generating Game Maps and Sprites from
Sentence Embeddings

Authors:Timothy Merino, Roman Negri, Dipika Rajesh, M Charity, Julian
Togelius
Download a PDF of the paper titled The Five-Dollar Model: Generating
Game Maps and Sprites from Sentence Embeddings, by Timothy Merino and
4 other authors
Download PDF

    Abstract: The five-dollar model is a lightweight text-to-image
    generative architecture that generates low dimensional images
    from an encoded text prompt. This model can successfully generate
    accurate and aesthetically pleasing content in low dimensional
    domains, with limited amounts of training data. Despite the small
    size of both the model and datasets, the generated images are
    still able to maintain the encoded semantic meaning of the
    textual prompt. We apply this model to three small datasets:
    pixel art video game maps, video game sprite images, and
    down-scaled emoji images and apply novel augmentation strategies
    to improve the performance of our model on these limited
    datasets. We evaluate our models performance using cosine
    similarity score between text-image pairs generated by the CLIP
    VIT-B/32 model.

Comments: to be published in AIIDE 2023
Subjects: Machine Learning (cs.LG); Computation and Language (cs.CL);
          Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2308.04052 [cs.LG]
          (or arXiv:2308.04052v1 [cs.LG] for this version)
          https://doi.org/10.48550/arXiv.2308.04052
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Roman Negri [view email]
[v1] Tue, 8 Aug 2023 05:16:51 UTC (2,007 KB)
Full-text links:

Download:

  * Download a PDF of the paper titled The Five-Dollar Model:
    Generating Game Maps and Sprites from Sentence Embeddings, by
    Timothy Merino and 4 other authors
    PDF
  * Other formats

[by-4]
Current browse context:
cs.LG
< prev   |   next >
new | recent | 2308
Change to browse by:
cs
cs.CL
cs.CV

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Reddit logo
(*) Bibliographic Tools

Bibliographic and Citation Tools

[ ] Bibliographic Explorer Toggle
Bibliographic Explorer (What is the Explorer?)
[ ] Litmaps Toggle
Litmaps (What is Litmaps?)
[ ] scite.ai Toggle
scite Smart Citations (What are Smart Citations?)
( ) Code, Data, Media

Code, Data and Media Associated with this Article

[ ] Links to Code Toggle
CatalyzeX Code Finder for Papers (What is CatalyzeX?)
[ ] DagsHub Toggle
DagsHub (What is DagsHub?)
[ ] Links to Code Toggle
Papers with Code (What is Papers with Code?)
[ ] ScienceCast Toggle
ScienceCast (What is ScienceCast?)
( ) Demos

Demos

[ ] Replicate Toggle
Replicate (What is Replicate?)
[ ] Spaces Toggle
Hugging Face Spaces (What is Spaces?)
( ) Related Papers

Recommenders and Search Tools

[ ] Link to Influence Flower
Influence Flower (What are Influence Flowers?)
[ ] Connected Papers Toggle
Connected Papers (What is Connected Papers?)
[ ] Core recommender toggle
CORE Recommender (What is CORE?)
[ ] IArxiv recommender toggle
IArxiv Recommender (What is IArxiv?)

  * Author
  * Venue
  * Institution
  * Topic

( ) About arXivLabs

arXivLabs: experimental projects with community collaborators

arXivLabs is a framework that allows collaborators to develop and
share new arXiv features directly on our website.

Both individuals and organizations that work with arXivLabs have
embraced and accepted our values of openness, community, excellence,
and user data privacy. arXiv is committed to these values and only
works with partners that adhere to them.

Have an idea for a project that will add value for arXiv's community?
Learn more about arXivLabs.

Which authors of this paper are endorsers? | Disable MathJax (What is
MathJax?)

  * About
  * Help

  * Click here to contact arXiv Contact
  * Click here to subscribe Subscribe

  * Copyright
  * Privacy Policy

  * Web Accessibility Assistance
  * arXiv Operational Status
    Get status notifications via email or slack