https://arxiv.org/abs/2306.00238

Skip to main content
Cornell University
We gratefully acknowledge support from
the Simons Foundation and member institutions.
 
arxiv logo > cs > arXiv:2306.00238
[                    ]

Help | Advanced Search

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

  * Login
  * Help Pages
  * About

Computer Science > Computer Vision and Pattern Recognition

arXiv:2306.00238 (cs)
[Submitted on 31 May 2023]

Title:Bytes Are All You Need: Transformers Operating Directly On File
Bytes

Authors:Maxwell Horton, Sachin Mehta, Ali Farhadi, Mohammad Rastegari
Download a PDF of the paper titled Bytes Are All You Need:
Transformers Operating Directly On File Bytes, by Maxwell Horton and
3 other authors
Download PDF

    Abstract: Modern deep learning approaches usually transform
    inputs into a modality-specific form. For example, the most
    common deep learning approach to image classification involves
    decoding image file bytes into an RGB tensor which is passed into
    a neural network. Instead, we investigate performing
    classification directly on file bytes, without the need for
    decoding files at inference time. Using file bytes as model
    inputs enables the development of models which can operate on
    multiple input modalities. Our model, \emph{ByteFormer}, achieves
    an ImageNet Top-1 classification accuracy of $77.33\%$ when
    training and testing directly on TIFF file bytes using a
    transformer backbone with configuration similar to DeiT-Ti ($72.2
    \%$ accuracy when operating on RGB images). Without modifications
    or hyperparameter tuning, ByteFormer achieves $95.42\%$
    classification accuracy when operating on WAV files from the
    Speech Commands v2 dataset (compared to state-of-the-art accuracy
    of $98.7\%$). Additionally, we demonstrate that ByteFormer has
    applications in privacy-preserving inference. ByteFormer is
    capable of performing inference on particular obfuscated input
    representations with no loss of accuracy. We also demonstrate
    ByteFormer's ability to perform inference with a hypothetical
    privacy-preserving camera which avoids forming full images by
    consistently masking $90\%$ of pixel channels, while still
    achieving $71.35\%$ accuracy on ImageNet. Our code will be made
    available at this https URL.

Subjects: Computer Vision and Pattern Recognition (cs.CV)
Cite as:  arXiv:2306.00238 [cs.CV]
          (or arXiv:2306.00238v1 [cs.CV] for this version)
          https://doi.org/10.48550/arXiv.2306.00238
          Focus to learn more
          arXiv-issued DOI via DataCite

Submission history

From: Maxwell Horton [view email]
[v1] Wed, 31 May 2023 23:18:21 UTC (7,209 KB)
Full-text links:

Download:

  * Download a PDF of the paper titled Bytes Are All You Need:
    Transformers Operating Directly On File Bytes, by Maxwell Horton
    and 3 other authors
    PDF
  * Other formats

(license)
Current browse context:
cs.CV
< prev   |   next >
new | recent | 2306
Change to browse by:
cs

References & Citations

  * NASA ADS
  * Google Scholar
  * Semantic Scholar

a export BibTeX citation Loading...

BibTeX formatted citation

x
[loading...          ]
Data provided by:

Bookmark

BibSonomy logo Mendeley logo Reddit logo ScienceWISE 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

[ ] 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?)

  * 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