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Gradient Amplification: An efficient way to train deep neural networks
Sunitha Basodi, Chunyan Ji, Haiping Zhang, Yi Pan
TL;DR
Deeper neural networks bring longer training times and vanishing gradients, while higher learning rates can risk suboptimal solutions. The paper proposes gradient amplification with an epoch-based training strategy, and reports improved accuracy faster, including at higher learning rates.
Problem
Deeper networks increase training time and can suffer vanishing gradients, while increasing learning rates to speed training risks suboptimal solutions or non-convergence.
Source: §1 · p. 1Method
The method dynamically amplifies gradients for selected layers during backpropagation and alternates amplification with ordinary training across epoch segments.
Source: §1 · pp. 1–2, §3 · p. 2Results
The authors report improved accuracy faster at higher learning rates, and amplified models outperform the original models’ mean and best accuracies across five runs.
Source: §1 · p. 1, §4.3 · p. 8Takeaways & Limitations
Experiments on VGG-19, ResNet-18, and ResNet-34 indicate that only a proportion of layers is sufficient for performance gains, with batch-normalization layers giving the best improvement among the tested layer types.
Source: §5 · p. 8
Abstract
from arXiv · showhide
Improving performance of deep learning models and reducing their training times are ongoing challenges in deep neural networks. There are several approaches proposed to address these challenges one of which is to increase the depth of the neural networks. Such deeper networks not only increase training times, but also suffer from vanishing gradients problem while training. In this work, we propose gradient amplification approach for training deep learning models to prevent vanishing gradients and also develop a training strategy to enable or disable gradient amplification method across several epochs with different learning rates. We perform experiments on VGG-19 and resnet (Resnet-18 and Resnet-34) models, and study the impact of amplification parameters on these models in detail. Our proposed approach improves performance of these deep learning models even at higher learning rates, thereby allowing these models to achieve higher performance with reduced training time.
1 INTRODUCTION
Deeper networks raise training-time and vanishing-gradient challenges, while higher learning rates can speed training but risk suboptimal solutions or non-convergence. The paper proposes intermittent gradient amplification and reports improved accuracy at higher learning rates, reached faster.
1 INTRODUCTION
- The method is reported to improve training and testing accuracy faster, even at higher learning rates, reducing training time.The introduction frames this as the outcome of alternating amplified and ordinary training.Source: §1 · p. 1
- Gradient amplification dynamically increases gradients for selected layers during backpropagation, alternating with ordinary training in other epochs.The stated aim is to propagate significant gradient values to initial layers.Source: §1 · pp. 1–2
- The paper’s contributions include experiments on amplification parameters and step-wise analyses of training strategies across different learning rates.The introduction also identifies the amplification method and intermittent training strategy as contributions.Source: §1 · pp. 1–2
2 BACKGROUND
The background describes vanishing gradients and learning-rate choices as challenges in training deep networks. It reviews existing approaches to gradients and learning-rate scheduling before introducing the paper’s fixed schedule across epochs.
2.1 Vanishing gradients
- Vanishing gradients can diminish during backpropagation toward initial layers, preventing weight updates and sometimes stopping training.The background names pretraining and fine-tuning, ReLU, batch normalization, ResNet residual connections, and hardware advances as approaches that help address the problem.Source: §2.1 · p. 2
2.2 Learning rates
- Higher learning rates can speed training but risk suboptimal solutions, whereas lower rates take longer and can achieve better optimal solutions.Schedulers lower rates over epochs; the paper’s next section uses a fixed learning-rate schedule across epochs to pursue higher accuracy at higher rates.Source: §2.2 · p. 2
- The background reviews learning-rate schedules and adaptive methods as ways to use learning rates during training.Examples include time-based, step, and exponential decay, as well as Adagrad, Adadelta, RMSprop, and Adam.Source: §2.2 · p. 2
3 PROPOSED METHOD
The proposed method multiplies gradients during backpropagation for a randomly selected subset of eligible layers. Its training process switches between amplified and ordinary backpropagation across epochs, with learning rate and amplification settings specified by epoch segments.
3 PROPOSED METHOD
- Gradients are multiplied by factor Γ in a randomly selected subset of eligible layers, with β setting the selected proportion.The eligible layer group can include convolution, batch-normalization, pooling, and activation layers; the chosen layer type affects performance.Source: §3 · pp. 2–3
- The training schedule specifies epoch endpoints, learning rate η, layer ratio β, and amplification factor Γ for each segment.A zero β denotes a segment without amplification; the method alternates amplified and ordinary training across epochs.Source: §3 · p. 3
- For each segment, the algorithm updates the learning rate, selects amplification layers when β is positive, and applies amplified or regular backpropagation before evaluation.The segment’s endpoint then sets the start epoch for the next segment.Source: §3 · p. 3
4 EXPERIMENTS & RESULTS
The experiments assess gradient amplification across VGG-19, ResNet-18, and ResNet-34, varying which layers are amplified, what fraction is selected, and the amplification factor. The strongest configurations outperform original models, with performance depending on the architecture and parameter choices.
4.1 Setup
- Experiments use CIFAR 10 with VGG-19, Resnet-18, and Resnet-34 for 150 epochs, beginning with 50 unamplified epochs and then using learning rates 0.1 and 0.01.The first 50 epochs are the initial transient; amplification is applied in later scheduled epochs.Source: §4.1 · p. 3, §4.2 · p. 4
4.2 Results
- The evaluation records five baseline runs, scans selected-layer ratios in step 1, then tests second-stage ratios and varies Γ on selected models.Step 1 tests amplification during epochs 51–100; step 2 extends it through epoch 130, with no amplification during epochs 131–150.Source: §4.2 · p. 4, §4.2.1 · p. 4, §4.1 · p. 4
4.2.1 Phase-1: (Effect of type of layers)
- VGG-19 improves with ReLU, performing best when BN layers are amplified; in Resnet-18 and Resnet-34, BN amplification improves performance, best when only BN layers from a residual block are selected.A reported improvement is 1.67% over the original model.Source: §4.2.1 · p. 6
4.2.2 Phase-2: (Effect of ratio of selected layers β)
- In step 2, all models beat baselines for most layer ratios, but ReLU+BN amplification often declines as ratios grow; small amplified-layer ratios can suffice.For VGG-19, performance initially increases with ratio and then decreases above 0.7; for ResNet-34, it declines above 0.8 except when both ReLU and BN are amplified, where declines occur at smaller ratios.Source: §4.2.2 · pp. 6–7
4.2.3 Phase-3: (Effect of gradient amplification factor)
- Accuracy is best around Γ=2; when Γ exceeds 5, models do not outperform or sometimes perform worse than their corresponding baselines.The effect of Γ also depends on the amplified-layer ratio; ratios close to 1 can reduce performance even at Γ values below 5.Source: §4.2.3 · p. 7
4.3 Best models
- Amplified models exceed both the five-run mean and best original-model accuracy; originals take longer to reach similar accuracy under the same settings.The comparison is against original models without amplification; the paper reports no overfitting in the amplified models.Source: §4.3 · p. 8, Table 1 · p. 8
5 CONCLUSIONS & FUTURE WORK
The paper concludes that gradient amplification with alternating amplified and unamplified epochs improves VGG-19, Resnet-18, and Resnet-34 performance, including at higher learning rates. It reports that a subset of layers suffices, with batch-normalization layers giving the strongest gains, and identifies larger datasets and exploding-gradient models as future work.
5 CONCLUSIONS & FUTURE WORK
- The proposed method and training strategy improve the original models’ performance and achieve better accuracies even at higher learning rates.The strategy switches between epochs with gradient amplification and epochs without it.Source: §5 · p. 8
- Experiments on VGG-19, Resnet-18, and Resnet-34 show that amplifying only a proportion of layers is sufficient for performance gains.Source: §5 · p. 8
- Batch-normalization layers give the best improvement, followed by ReLU layers, while performance quickly diminishes when both are amplified.Source: §5 · p. 8
- Future work will test larger datasets, including CIFAR-100 and ImageNet, and explore dynamically diminishing gradients in models with exploding gradients.The experimental models studied here had no exploding gradients.Source: §5 · p. 8