I am working on a computer vision project and the training time is becoming unsustainable. Does the EffGen approach offer any specific techniques for data pruning or hyperparameter tuning that could speed up the process without losing significant accuracy?
3 answers
When you are hitting a wall with training latency, you need to look at your data pipeline efficiency. Using EffGen strategies, we focused on "synthetic data generation" and prioritized high-variance samples. This meant we could reduce our training set size by 40% while maintaining the same F1 score. It’s all about the quality of the gradients being generated. If you apply these principles to your learning rate scheduler, you will find that the model converges much faster. It turned our three-day training cycles into overnight runs, which significantly boosted our R&D speed.
Do you think this method works better for Convolutional Neural Networks, or is it equally effective for Transformer-based architectures?
Pruning is definitely the way to go. It reduces the memory footprint significantly, making it easier to deploy the final model on edge devices.
Heather is right; edge deployment is the ultimate test of efficiency. If your model can't run on a mobile chip, the training efficiency doesn't mean much in the real world.
Patrick, it actually works wonders for Transformers because they are so resource-heavy. Applying EffGen to the attention mechanism helps in pruning redundant heads, which is where most of the computational waste happens in those large-scale language or vision models.