The Hidden Influence of Neural Network Initialization

Neural networks are often seen as black boxes, but there's a fascinating layer beneath the surface. Research has now unveiled the hidden impact of random initializations on the final performance of these models. A important discovery is the concept of 'initialization memory,' which affects how a trained network remembers its starting conditions.

Unpacking Initialization Memory

In controlled experiments using ResNet architectures on the CIFAR-10 dataset, researchers measured how varying the scale of random initializations influenced the network's final performance. They found a striking variance in test accuracy, up to 26.5 percentage points, despite achieving over 99.5% training accuracy. This discrepancy suggests that something deeper than undertraining is at play.

Here's the kicker: the initialization's impact doesn't vanish with longer training durations. Extending the training regime to 5,000 epochs didn't significantly alter the spread in performance. Low-learning-rate Stochastic Gradient Descent (SGD) can memorize its initial conditions, while adaptive methods like Adam seem to wipe this memory clean.

The Role of Learning Dynamics

Why does this matter? The key finding here's that the learning dynamics dictate the persistence of this initialization memory. Gradient-flow-like dynamics, characteristic of low-learning-rate SGD, preserve it. In contrast, stochastic finite-step effects, explicit norm decay, and adaptive preconditioning, typical of Adam-family methods, erase it. This points to a deeper understanding: the network's practical inductive bias is shaped by both its architectural prior and the training pipeline's forgetting dynamics.

So, what's the takeaway for practitioners? The same regularizers that enhance generalization are those that obliterate initialization memory. This isn't just academic curiosity, it's a practical insight. Understanding the nuances of initialization could guide how practitioners choose training protocols and optimize networks for specific tasks.

Implications for Training Practices

One question arises: Should we care more about initialization strategies? If specific initialization scales can lead to significantly varied outcomes, then perhaps more attention should be given to this often-overlooked step. It's clear that initialization isn't a mere formality but a critical component that can shape the trajectory of learning.

This research importantly shifts the perspective on neural network training. The initialization isn't just a starting point, but a significant factor that interacts with training dynamics to define the network's final state. It challenges conventional wisdom that often downplays the importance of how we set the stage for learning.