# Simplified Machine Learning Model as an Intelligent Support for Safe Urban Cycling
## 2 Simplified Machine Learning
### Approach Overview and Contributions  
Contrastive learning, as mentioned by [PERSON], [PERSON] et al. [13], mimics the way humans learn and aims to learn low-dimensional representations of data by contrasting between similar and dis-similar samples. Therefore, humans can learn new things with a small set of examples. When a person is presented with new examples, they are able to understand new concepts quickly and will then recognize variations of those concepts in the future. Just like that, a child can learn to recognize a cat from a single image. However, currently, a machine learning system needs many examples to learn the characteristics of a cat and recognize them in the future or in other examples. We can observe that in standard associative learning, an animal must repeatedly experience a series of associations between a stimulus and a consequence before it completely learns a particular stimulus. Therefore, learning is inevitably incremental. However, animals sometimes conclude or infer results that they have never even observed before and from which they need to learn quickly to survive. In such cases, animals can learn from a single exposure to the stimulus in this situation, but making an analogy with machine learning, this is what we have defined as _simplified machine learning_,[PERSON] et al. [19; 20] and generally, it is known in the literature as one-time learning [21]. Simplified machine learning is particularly advantageous for various applications where traditional machine learning models are insufficient due to limitations in the amount of data or the need for rapid adaptation and flexibility. **Medicine:** In this field, its application focuses on supporting health professionals in the diagnosis of rare diseases using a limited number of medical images as training examples, which supports identifying conditions using minimal or scarce data. On the other hand, it could accelerate the development of more precise treatments, drugs, and diagnoses. **Security systems and access control:** Within facial recognition systems, being able to identify a person from a single image is essential in situations such as access control, where it is very likely that there are no several images available. It also helps improve biometric authentication systems by being able to verify identities using minimal training samples. **Handwriting recognition:** In document scanning systems, it enables the recognition of handwritten or uncommon fonts using limited examples, enabling the scanning of historical or old documents. It also supports the classification and understanding of textual data with minimal labeled examples using only a limited set of examples. **Robotics:** Using object recognition with few examples, robots can identify and manipulate new objects with minimal training. This functionality allows for increased adaptability in non-uniform or dynamic environments such as commercial warehouses. Additionally, the ability of robots to learn tasks from a single demonstration facilitates the learning of complex tasks using few examples. Likewise, autonomous robotics uses recognition with few examples to detect or avoid unknown objects in its path for better self-driving. **Species identification:** It can support the identification and tracking of rare or completely unidentified species from a few photographic samples. Furthermore, it facilitates the study of biodiversity through the recognition of new species from limited observations or images. Figure 3: Contrast losses. Self-supervised contrast loss contrasts a single positive image for each anchor (i.e., a magnified version of the same image) with a set of negative images from the sample set (**a**). However, the supervised contrast loss (**b**) considered in this article contrasts the set of all samples of the same class as positive with the negative ones of the rest of the set of images.