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Deep Learning For Vision

Heuristics for Faster Training

1. Introduction

Training deep neural networks can be computationally expensive and time-consuming. Heuristics for faster training aim to accelerate convergence while maintaining or improving the model's generalization and accuracy.

2. Key Heuristics for Faster Training

2.1 Proper Weight Initialization

  • Use Xavier/Glorot Initialization for sigmoidal/tanh activations.

  • Use He Initialization for ReLU and variants.

  • Good initialization preserves signal variance across layers and avoids vanishing or exploding gradients, accelerating learning at early stages.

2.2 Adaptive Learning Rate Methods

  • Optimizers like Adam, RMSProp, Adagrad dynamically adapt the learning rate for each parameter.

  • They converge faster than standard SGD by scaling updates based on gradient history.

2.3 Learning Rate Scheduling

  • Use schedules like step decay, exponential decay, cyclic learning rates, or warm restarts.

  • Start with a larger learning rate to explore the parameter space, then gradually reduce the learning rate for fine-tuning.

2.4 Batch Normalization

  • Normalizes layer inputs to maintain zero mean and unit variance.

  • Mitigates internal covariate shift, stabilizing learning and allowing for higher learning rates, thus faster convergence.

2.5 Mini-batch Training

  • Training with mini-batches balances stochastic noise and computational efficiency.

  • Larger mini-batches improve parallelism on GPUs but can slow convergence on generalization; tuning batch size helps optimize training speed and model quality.

2.6 Gradient Clipping

  • Prevents exploding gradients by clipping gradients to a fixed threshold.

  • Allows stable updates in deep or recurrent networks and speeds training indirectly by avoiding unstable steps.

2.7 Skip Connections and Better Architectures

  • Networks like ResNet facilitate gradient flow and speed training of very deep models.

  • Architectural advances ensure effective backpropagation and reduce training iterations needed for convergence.

2.8 Early Stopping and Checkpointing

  • Monitors validation loss during training and stops early if no improvement is seen, saving computation time.

  • Checkpointing enables resuming training without starting over (useful in long trainings).

3. Additional Tips

  • Use mixed precision training leveraging lower precision arithmetic to speed up training on compatible hardware.

  • Efficient data pipeline and augmentation improve throughput.

  • Use transfer learning to initialize models with pretrained weights, requiring fewer epochs on new tasks.

4. Summary Table

HeuristicDescriptionEffect on Training
Weight InitializationXavier, He methodsPrevent slow start and gradient issues
Adaptive OptimizersAdam, RMSProp, AdagradFaster convergence, adaptive step sizes
Learning Rate SchedulesStep decay, cyclic LR, warm restartsBalance exploration and refinement
Batch NormalizationNormalize activations each mini-batchFaster, stable training with higher learning rates
Mini-batchesSmall batches for gradient estimatesEfficient updates with noise to escape bad minima
Gradient ClippingLimit large gradientsAvoid unstable updates and training divergence
Skip ConnectionsFacilitate gradient flow in very deep networksFaster training by solving vanishing gradients
Early StoppingStop early on no validation improvementSave time, avoid overfitting

5. Conclusion

Faster training in deep learning is achieved by combining multiple heuristics such as good initialization, adaptive optimizers, normalization, and architecture design. These heuristics enhance convergence speed, stability, and overall model performance.