Final Masters' Project: Generative Approach to Abstraction and Reasoning Challenge
This repository contains the code used on the development ambit of my Final Masters' Degree Project, since as I started to have multiple processes that made use of the same code, having all instances of the code aligned became more problematic and time-consuming than just taking the tame to generate a centralized code base.
Generative Approach to Abstraction and Reasoning Challenge (whose beautiful acronym, GAARC, will be used from now on to refer to it) aims to be a proposal to the (Abstracted And Reasoning Challenge)[https://www.kaggle.com/c/abstraction-and-reasoning-challenge], introduced by François Chollet, that aims to properly follows a Generalistic approach. The complete extent of the approach is explained within the Final Master's Degree Project's document, which will be, if possible, linked here upon finalization.
As a brief overlook, the approach of this project is to try to achieve, through multi-task learning, a rich hidden space that captures a complex enough interpretation of the data so that the tasks can be confronted through few-shot learning over the required changes in that hidden space interpretation.
TBD
gaarc/
├── data/
│ ├── arc_data_models.py
│ ├── augmentation.py
│ ├── data_interface.py
│ ├── preprocessing.py
│ └── transformations.py
├── model/
│ ├── autoencoder.py
│ └── unet.py
└── visualization/
└── arc_visualization.py
notebooks/
- data: Data management, from preprocessing and transformations for augmentation to data interface classes.
- model: Everything related to model architecture definition and management automation.
- visualization: General visualization tools for the rest of the components.
The notebooks were executed in Kaggle within the context of the competition and, thus, do not resolve data acquisition, and this will need to be provided to the local environment if executed outside of Kaggle.