Self-Supervised Learning Risks

Self-Supervised Learning Risks refer to potential challenges and vulnerabilities associated with self-supervised learning methods in artificial intelligence and machine learning. These risks include data quality concerns, model overfitting, and the propagation of biases present in training data. Additionally, self-supervised models may generate misleading or inaccurate representations, leading to poor decision-making in downstream tasks. Understanding these risks is crucial for researchers and practitioners to develop robust, ethical AI systems that leverage self-supervised learning while mitigating potential pitfalls.

‍