Bibliography (16):

1. NIMA: Neural Image Assessment

2. A deep architecture for unified esthetic prediction

3. Predicting and Understanding Urban Perception with Convolutional Neural Networks

4. Deep Learning the City: Quantifying Urban Perception At A Global Scale

5. StreetNet: Preference Learning with Convolutional Neural Network on Urban Crime Perception

6. ​ ‘CLIP’ directory

7. CLIP: Learning Transferable Visual Models From Natural Language Supervision

8. DALL·E 1: Creating Images from Text: We’ve trained a neural network called DALL·E that creates images from text captions for a wide range of concepts expressible in natural language

9. Multimodal Neurons in Artificial Neural Networks [CLIP]

10. Wikipedia Bibliography:

    1. Airbnb

    2. Statistical-significance

    3. Convolutional neural network

    4. https://en.wikipedia.org/wiki/Visual_design_elements_and_principles  :

       https://en.wikipedia.org/wiki/Visual_design_elements_and_principles

    5. https://en.wikipedia.org/wiki/Figure-ground  :

       https://en.wikipedia.org/wiki/Figure-ground

    6. https://en.wikipedia.org/wiki/Rule_of_thirds  :

       https://en.wikipedia.org/wiki/Rule_of_thirds

    7. https://en.wikipedia.org/wiki/Contrast_(vision)  :

       https://en.wikipedia.org/wiki/Contrast_(vision)