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Title: Stochastic Weight Averaging in Parallel: Large-Batch Training That Generalizes Well

Paper content:
We propose Stochastic Weight Averaging in Parallel (SWAP), an algorithm to accelerate DNN training.
Our algorithm uses large mini-batches to compute an approximate solution quickly and then refines it by averaging the weights of multiple models computed independently and in parallel.
The resulting models generalize equally well as those trained with small mini-batches but are produced in a substantially shorter time.
We demonstrate the reduction in training time and the good generalization performance of the resulting models on the computer vision datasets CIFAR10, CIFAR100, and ImageNet.
Stochastic gradient descent (SGD) and its variants are the de-facto methods to train deep neural networks (DNNs).
Each iteration of SGD computes an estimate of the objective's gradient by sampling a mini-batch of the available training data and computing the gradient of the loss restricted to the sampled data.
A popular strategy to accelerate DNN training is to increase the mini-batch size together with the available computational resources.
Larger mini-batches produce more precise gradient estimates; these allow for higher learning rates and achieve larger reductions of the training loss per iteration.
In a distributed setting, multiple nodes can compute gradient estimates simultaneously on disjoint subsets of the mini-batch and produce a consensus estimate by averaging all estimates, with one synchronization event per iteration.
Training with larger mini-batches requires fewer updates, thus fewer synchronization events, yielding good overall scaling behavior.
Even though the training loss can be reduced more efficiently, there is a maximum batch size after which the resulting model tends to have worse generalization performance (McCandlish et al., 2018; Keskar et al., 2016; Hoffer et al., 2017; Golmant et al., 2018; Shallue et al., 2018) .
This phenomenon forces practitioners to use batch sizes below those that achieve the maximum throughput and limits the usefulness of large-batch training strategies.
Stochastic Weight Averaging (SWA) ) is a method that produces models with good generalization performance by averaging the weights of a set of models sampled from the final stages of a training run.
As long as the models all lie in a region where the population loss is mostly convex, the average model can behave well, and in practice, it does.
We have observed that if instead of sampling multiple models from a sequence generated by SGD, we generate multiple independent SGD sequences and average models from each, the resulting model achieves similar generalization performance.
Furthermore, if all the independent sequences use small-batches, but start from a model trained with large-batches, the resulting model achieves generalization performance comparable with a model trained solely with small-batches.
Using these observations, we derive Stochastic Weight Averaging in Parallel (SWAP): A simple strategy to accelerate DNN training by better utilizing available compute resources.
Our algorithm is simple to implement, fast and produces good results with minor tuning.
For several image classification tasks on popular computer vision datasets (CIFAR10, CIFAR100, and ImageNet), we show that SWAP achieves generalization performance comparable to models trained with small-batches but does so in time similar to that of a training run with large-batches.
We use SWAP on some of the most efficient publicly available models to date, and show that it's able to substantially reduce their training times.
Furthermore, we are able to beat the state of the art for CIFAR10 and train in 68% of the time of the winning entry of the DAWNBench competition.

assistant: text: We propose SWAP, a distributed algorithm for large-batch training of neural networks.