Click Here to download the base paper on image recognition projects for final-year students.
Synthetic image recognition datasets offer unmatched advantages for designing and evaluating deep learning projects of neural networks: they make it possible to (i) render as many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and captions), (iii) precisely control distribution shifts between training and testing to isolate variables of interest for sound experimentation. Despite such promise, the use of synthetic image data is still limited — and often played down — mainly due to their lack of realism. Most works, therefore, rely on datasets of real images, which have often been scraped from public images on the internet and may have issues concerning privacy, bias, and copyright, while offering little control over how objects precisely appear. In this work, we present a path to democratize the use of photorealistic synthetic data: we develop a new generation of interactive environments for representation learning research, that offer both controllability and realism.
By achieving photorealism, the synthetic data produced by PUG aims to closely resemble actual images, providing a diverse and realistic training dataset for machine learning algorithms. Simultaneously, the project emphasizes semantic controllability, enabling researchers and practitioners to manipulate specific features or characteristics within the synthetic data. This control over semantics is crucial for training models to recognize and understand specific objects, patterns, or attributes. The utilization of such synthetic data is instrumental in addressing challenges related to data scarcity and privacy concerns, as it enables the development and training of robust machine learning models without reliance on large amounts of real-world data. PUG contributes to the advancement of representation learning by offering a valuable resource for model training and evaluation, ultimately enhancing the generalization and performance of machine learning systems across various applications. The utilization of such synthetic data is instrumental in addressing challenges related to data scarcity and privacy concerns, as it enables the development and training of robust machine learning models without reliance on large amounts of real-world data. PUG contributes to the advancement of representation learning by offering a valuable resource for model training and evaluation, ultimately enhancing the generalization and performance of machine learning systems across various applications.
We use the Unreal Engine, a powerful game engine well known in the entertainment industry, to produce PUG (Photorealistic Unreal Graphics) environments and datasets for representation learning. In this paper, we demonstrate the potential of PUG to enable more rigorous evaluations of vision models.
