The lottery ticket hypothesis suggests that within a larger neural network, there exists a smaller, randomly initialized subnet (a "winning ticket") that can train to achieve similar or better performance than the original larger network. According to the hypothesis, by finding this subnet and training it from scratch, the model can achieve faster convergence and better performance.
This idea challenges the common practice of training large networks from scratch, suggesting that smaller networks initialized in certain ways can be just as effective. Researchers have explored pruning techniques and initialization strategies to discover these "winning tickets" in neural networks.
The lottery ticket hypothesis has implications for efficient network design, model compression, and the understanding of how neural networks learn.