# Semiotics Networks Representing Perceptual Inference

arXiv: https://arxiv.org/abs/2310.05212 · HTML: https://mlsanity.com/paper/2310.05212/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 38 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Builds on (most cited first, 12 of 12)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Methods for Interpreting and Understanding Deep Neural Networks](https://mlsanity.com/paper/1706.07979/) (2017)
- [Overparameterized Neural Networks Implement Associative Memory](https://mlsanity.com/paper/1909.12362/) (2019)
- [Adversarial Noise Layer: Regularize Neural Network By Adding Noise](https://mlsanity.com/paper/1805.08000/) (2018)
- [Denoising Adversarial Autoencoders](https://mlsanity.com/paper/1703.01220/) (2017)
- [Latent Space Explanation by Intervention](https://mlsanity.com/paper/2112.04895/) (2021)
- [Interpreting Deep Classifier by Visual Distillation of Dark Knowledge](https://mlsanity.com/paper/1803.04042/) (2018)
- [Denoising and Verification Cross-Layer Ensemble Against Black-box Adversarial Attacks](https://mlsanity.com/paper/1908.07667/) (2019)
- [Autoencoder Attractors for Uncertainty Estimation](https://mlsanity.com/paper/2204.00382/) (2022)
- [AlteregoNets: a way to human augmentation](https://mlsanity.com/paper/1901.09786/) (2019)

## Abstract (arXiv)

Every day, humans perceive objects and communicate these perceptions through various channels. In this paper, we present a computational model designed to track and simulate the perception of objects, as well as their representations as conveyed in communication. We delineate two fundamental components of our internal representation, termed "observed" and "seen", which we correlate with established concepts in computer vision, namely encoding and decoding. These components are integrated into semiotic networks, which simulate perceptual inference of object perception and human communication. Our model of object perception by a person allows us to define object perception by em a network. We demonstrate this with an example of an image baseline classifier by constructing a new network that includes the baseline classifier and an additional layer. This layer produces the images "perceived" by the entire network, transforming it into a perceptualized image classifier. This facilitates visualization of the acquired network. Within our network, the image representations become more efficient for classification tasks when they are assembled and randomized. In our experiments, the perceptualized network outperformed the baseline classifier on MNIST training databases consisting of a restricted number of images. Our model is not limited to persons and can be applied to any system featuring a loop involving the processing from "internal" to "external" representations.