[Paper Review] Limits of End-to-End Learning

Tobias Glasmachers|arXiv (Cornell University)|Apr 26, 2017

Neural Networks and Applications26 references50 citations

TL;DR

The paper argues that end-to-end learning (e2e) has limitations as networks become more modular, showing experiments where e2e training fails or becomes inefficient, and suggests structured, module-aware training as a needed alternative.

ABSTRACT

End-to-end learning refers to training a possibly complex learning system by applying gradient-based learning to the system as a whole. End-to-end learning system is specifically designed so that all modules are differentiable. In effect, not only a central learning machine, but also all "peripheral" modules like representation learning and memory formation are covered by a holistic learning process. The power of end-to-end learning has been demonstrated on many tasks, like playing a whole array of Atari video games with a single architecture. While pushing for solutions to more challenging tasks, network architectures keep growing more and more complex. In this paper we ask the question whether and to what extent end-to-end learning is a future-proof technique in the sense of scaling to complex and diverse data processing architectures. We point out potential inefficiencies, and we argue in particular that end-to-end learning does not make optimal use of the modular design of present neural networks. Our surprisingly simple experiments demonstrate these inefficiencies, up to the complete breakdown of learning.

Motivation & Objective

• Explain the motivation for questioning the scalability of end-to-end learning as networks grow in modular complexity.
• Assess how coupling between modules can hinder learning in e2e setups.
• Demonstrate through experiments that e2e training can become exponentially harder or fail for multi-module systems.
• Propose that training should respect network structure and consider modular, staged training approaches.

Proposed method

• The paper provides a conceptual critique of end-to-end learning and outlines two experimental setups to stress-test e2e training on modular networks.
• Experiment 1 uses scalable stacking of small modules to show training difficulty grows roughly exponentially with the number of stacked modules.
• Experiment 2 uses a planning task in a RoboRally-like grid world to create a two-module system where mutual dependencies hinder joint learning.
• Training performance is compared between end-to-end training and modular, sequential training approaches.
• The authors analyze how gradient signals propagate through nested modules and how non-trivial inter-module couplings can degrade learning.

Experimental results

Research questions

• RQ1 Does end-to-end learning scale efficiently when networks are composed of many interacting modules?
• RQ2 Can end-to-end training break down for moderately complex modular architectures?
• RQ3 Can training modules separately (or in a structured order) overcome observed failures of e2e learning?
• RQ4 What are the practical implications for designing large, multi-component learning systems?

Key findings

• End-to-end learning can become inefficient as network complexity grows, with training effort growing exponentially in the number of stacked modules.
• End-to-end learning can fail completely for multi-module architectures, leaving all modules near their initial weights.
• Training modules one at a time, or in a structured sequence aligned with the network design, can avoid or mitigate breakdowns observed in e2e training.
• Even small, well-defined modules learn reliably when other modules are already trained and frozen, highlighting the benefit of a decomposed training strategy.
• A planning-based two-module system in a deterministic environment demonstrates mutual dependency between modules that can prevent joint learning without staged training.