EndtoEndLernen

EndtoEndLernen, often written as End-to-End Lernen or End-to-End-Lernen, is a paradigm in machine learning where a system learns to map inputs to outputs directly, with minimal reliance on manually designed intermediate representations. In practice, this means training a single model or a closely integrated set of models jointly, using backpropagation to optimize a task-specific loss over the end-to-end output.

Compared with traditional modular pipelines, which separate feature extraction, modeling, and decision logic, end-to-end approaches aim to optimize the whole process simultaneously. This reduces hand-engineered components but can reduce interpretability and require large datasets and compute.

Applications include automatic speech recognition, machine translation, and computer vision tasks such as image captioning, as well as robotics and autonomous systems where perception, planning, and control can be trained end-to-end, often with differentiable simulators or reinforcement learning components.

Advantages of end-to-end learning include a simpler pipeline and potential performance gains, along with easier adaptation to new tasks given sufficient data. Challenges encompass data and compute demands, the risk of learning spurious correlations, difficulty in troubleshooting, and potential bias amplification.

Techniques commonly used in end-to-end learning include deep neural networks, transformer and sequence-to-sequence models, differentiable programming, and reinforcement learning. Current trends emphasize transfer learning, pretraining on large datasets, and architectural inductive biases to improve data efficiency and generalization.