@inproceedings{
modi2024asynchronous,
title={Asynchronous Perception Machine for Efficient  Test Time Training},
author={Rajat Modi and Yogesh S Rawat},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=7Ye12RLZ4P}
}
In this work, we propose Asynchronous Perception Machine (APM), a computationally-efficient architecture for test-time-training (TTT). APM can process patches of an image one at a time in any order asymmetrically, and still encode semantic-awareness in the net. We demonstrate APM’s ability to recognize out-of-distribution images without dataset-specific pre-training, augmentation or
any-pretext task. APM offers competitive performance over existing TTT approaches. To perform TTT, APM just distills test sample’s representation once. APM possesses a unique property: it can learn using just this single representation and starts predicting semantically-aware features. APM’s ability to recover semantic information from a global CLS token validates the insight that CLS
tokens encode geometric-information of a given scene and can be recovered using appropriate inductive-biases. This offers a novel-insight with consequences for representational-learning. APM demostrates potential applications beyond test-time-training: APM can scale up to a dataset of 2D images and yield semantic-clusterings in a single forward pass. APM also provides first empirical evidence towards validating Hinton at Al’s GLOM’s insight, i.e. if input percept is a field. Therefore, APM helps our community converge towards an implementation which can do both interpolation and perception on a shared-connectionist hardware. Our
codebase has been made available at https://rajatmodi62.github.io/apm_project_page/

--------

**It now appears that some of the ideas in GLOM could be made to work.**

https://www.technologyreview.com/2021/04/16/1021871/geoffrey-hinton-glom-godfather-ai-neural-networks/

```
   .-""""""-.
 .'          '.
/   O      O   \
|      O       |
 \  '------'  /
  '.        .'
    '-....-'
A silent man in deep-contemplation.
Silent man emerges only sometimes.
And he loves all.
```
Asynchronous Perception Machine for Efficient  Test Time Training
Rajat Modi Yogesh S Rawat