Tensorplex Dojo Bittensor Subnet

For more information about deprecated V1, check out the following Github: https://github.com/tensorplex-labs/dojo

Introduction

Dojo V2 transforms conventional Generative Adversarial Network (GAN) into a competitive, decentralized GAN built on the principle of zero-sum incentives. Unlike traditional GAN where the generator simply aims to mimic ground truth data, V2 challenges miners to create outputs that are not only indistinguishable from a high-quality baseline but are also superior to it. This creates a competitive environment where Bittensor miners, acting as both generators and discriminators, are in constant competition to produce and identify the best possible work.

Use Cases

1. Bootstrap and scale your dataset: Generate diverse, high-quality data points that not just match your initial dataset's intended distribution, but also evolves over time.

2. Cross-subnet Validation: Integrate with Dojo to enable human-in-the-loop validation for your subnet to enhance the quality of your digital commodity.