mtt-distillation
The 'mtt-distillation' project employs a cutting-edge technique in 'Dataset Distillation,' optimizing synthetic images to emulate the training behaviours of genuine datasets. This ensures similar performance during tests. Leveraging expert networks, the project converts synthetic data for tasks such as ImageNet subset synthesis and texture creation, broadening AI model capabilities while conserving resources. The project's scalable solutions are suitable for areas like fashion and targeted image sets due to its tileable textures. Highlighted features include the generation of class-based textures, solid training model frameworks, and integration possibilities with various datasets, enhancing the effectiveness of synthetic dataset usage.
