Deep supervised learning using local errors (1711.06756v1)

Abstract: Error backpropagation is a highly effective mechanism for learning high-quality hierarchical features in deep networks. Updating the features or weights in one layer, however, requires waiting for the propagation of error signals from higher layers. Learning using delayed and non-local errors makes it hard to reconcile backpropagation with the learning mechanisms observed in biological neural networks as it requires the neurons to maintain a memory of the input long enough until the higher-layer errors arrive. In this paper, we propose an alternative learning mechanism where errors are generated locally in each layer using fixed, random auxiliary classifiers. Lower layers could thus be trained independently of higher layers and training could either proceed layer by layer, or simultaneously in all layers using local error information. We address biological plausibility concerns such as weight symmetry requirements and show that the proposed learning mechanism based on fixed, broad, and random tuning of each neuron to the classification categories outperforms the biologically-motivated feedback alignment learning technique on the MNIST, CIFAR10, and SVHN datasets, approaching the performance of standard backpropagation. Our approach highlights a potential biological mechanism for the supervised, or task-dependent, learning of feature hierarchies. In addition, we show that it is well suited for learning deep networks in custom hardware where it can drastically reduce memory traffic and data communication overheads.

• The paper introduces local error signals via fixed random auxiliary classifiers to bypass conventional backpropagation.
• The approach achieves competitive results on datasets like MNIST while reducing computational and hardware overhead.
• The work offers a biologically plausible learning model that may advance efficient hardware implementations of deep neural networks.

Deep Supervised Learning Using Local Errors

The paper by Mostafa, Ramesh, and Cauwenberghs presents an innovative approach to learning in deep neural networks by leveraging local errors instead of traditional backward error propagation. Standard backpropagation, while effective for optimizing deep networks, presents certain biological limitations, such as the necessity for neuron state buffering and strict weight symmetry, which are not feasible in biological neural circuits. This paper proposes a learning paradigm where each layer generates its own errors based on local classifier feedback, eliminating the need for global error backpropagation.

Summary of Proposed Method

The authors introduce a model approximating supervised learning using local errors facilitated by fixed, random auxiliary classifiers. This method departs from a global, uniform objective function typical in backpropagation, aiming instead to optimize separate local objective functions layer by layer. Each layer independently learns through error signals generated by local classifiers; these classifiers incorporate random but fixed weights corresponding to each neuron’s activation, which bypass the need for synchronization across layers in weight updates.

The technique involves using a simple classifier at each layer with fixed random weights that transform layer activations into predicted class scores. Error signals, determined by the mismatch between these scores and the actual class labels, update the layer's weights. With this decoupled approach, learning can occur either in a staggered, layer-by-layer manner or across all layers simultaneously based on local error signals.

Key Results and Findings

The paper substantiates its proposed methodology with numerical results from well-known datasets such as MNIST, CIFAR10, and SVHN. The authors claim that their local error-based learning paradigm closely competes with backpropagation in performance, with notable advantages over biologically motivated feedback alignment techniques. Specifically, the proposed method reduces computational and memory burdens by removing the requirements for backpropagated errors and the associated backward passes.

On the MNIST dataset, networks trained using local error signals approach the performance of those trained with conventional backpropagation, while slightly underperforming on more complex datasets like CIFAR10 and SVHN. However, even though performance is slightly lower than that of backpropagation, the approach presented offers significant improvements in hardware implementation efficiency and better aligns with biologically plausible learning mechanisms.

Implications for Hardware and Biological Networks

An essential advantage of using local errors is the drastic reduction in memory traffic and parameter synchronization typically required in deep learning hardware computations. By generating error signals in a localized manner, this method substantially alleviates the bandwidth and communication overhead required during training, which is crucial for implementing deep learning models on resource-constrained or custom hardware platforms.

From a theoretical viewpoint, this approach suggests a plausible pathway for simulating certain aspects of biological learning mechanisms in artificial systems. With neurons in biological networks not relying on error signals propagated backward over long distances, the presented model mimics such localized interactions seen in biological networks more closely than traditional methods.

Future Directions

The exploration of local error signals as a substitute for elaborate backpropagation opens numerous opportunities for future research and improvements. As deep neural networks grow in complexity, optimizing local learning strategies could complement hierarchical feature composition and further close the gap between biological and artificial learning. Future work could investigate adaptability to other types of networks or integration with other contemporary techniques, potentially leading to more naturally inspired AI systems.

In summary, the research offers a compelling reinforcement of local error-based learning as a potential alternative to standard backpropagation, with promising implications for both the theoretical understanding of neural computation and practical applications in AI and neural network hardware design.