Teacher Detector

Teacher detectors leverage the knowledge of a more complex, high-performing "teacher" model to train a simpler, more efficient "student" model, aiming to transfer accuracy while reducing computational cost and resource demands. Current research focuses on improving knowledge distillation techniques across various architectures, including convolutional neural networks and transformers, for tasks such as object detection, anomaly detection, and fake news identification. This approach holds significant promise for deploying accurate and efficient models in resource-constrained environments and for accelerating training processes in computationally intensive applications.