Overview of UDA

Unsupervised Data Augmentation (UDA) is a semi-supervised learning technique that enhances model performance on both language and vision tasks. Astonishingly, UDA achieves state-of-the-art results using only 20 labeled examples, significantly outperforming previous leading techniques that required a massive 25,000 labeled samples. It’s a remarkable testament to how the right approach can let you achieve more with less.

Why UDA Works

The magic of UDA lies in its ability to minimize the need for labeled examples while optimizing the use of unlabeled data. Consider this analogy: imagine you’re trying to cook a gourmet dish with only a pinch of salt while having copious amounts of spices. Traditionally, you’d need a lot of accuracy from the salt. But UDA allows you to unleash the full potential of those spices, drastically improving the final meal with minimal salt – or in our case, labeled examples.

Understanding UDA’s Performance

UDA has proven its prowess through various metrics:

• On IMDb: UDA achieved an error rate of 4.20% with only 20 labeled examples, compared to 4.32% and 4.51% from other models that used 25,000 labeled examples.
• CIFAR-10 and SVHN: UDA reduced the error rate to 4.31% and 2.28%, respectively.
• ImageNet: Top-1 accuracy jumped to an impressive 68.78% and top-5 accuracy to 88.80%.

Getting Started with UDA

Ready to dive into UDA? We have several components to help you hit the ground running:

• Code for text classification based on BERT
• Code for image classification on CIFAR-10 and SVHN
• Checkpoints and code for back translation augmentation

This code is designed to work seamlessly on GPU and Google Cloud TPU environments.

Requirements

To get started, ensure that you have:

• Python 2.7 with TensorFlow 1.13 installed.
• Run the following to install required dependencies:

pip install --user absl-py

Image Classification Setup

Preprocessing Data

Your journey begins with preprocessing data. Here’s how to automatically generate augmented examples:

• Navigate to the image directory and execute:

AUG_COPY=100 bash scripts/download_cifar10.sh $AUG_COPY

• This will require approximately 120GB of disk space. You can adjust the AUG_COPY variable for fewer examples if needed.

CIFAR-10 and SVHN Execution

To run UDA with CIFAR-10, execute:

bash scripts/run_cifar10_gpu.sh --aug_copy=$AUG_COPY

For SVHN, do the following:

bash scripts/run_svhn_gpu.sh --aug_copy=$AUG_COPY

Text Classification on GPU

Addressing Memory Issues

Text levels in movie reviews can hinder performance due to sequence memory limitations. If you’re facing memory constraints with your GPU, we provide the following steps:

• Adjust the maximum sequence length
• Utilize smaller batch sizes

For instance, when you’re ready to run UDA with BERT, go to the text directory and execute:

# Set a larger max_seq_length as needed
MAX_SEQ_LENGTH=128
# Download data and pretrained BERT checkpoints
bash scripts/download.sh
# Preprocessing
bash scripts/prepro.sh --max_seq_length=$MAX_SEQ_LENGTH
# Baseline accuracy: around 68%
bash scripts/run_base.sh --max_seq_length=$MAX_SEQ_LENGTH
# UDA accuracy: around 90%
bash scripts/run_base_uda.sh --train_batch_size=8 --max_seq_length=$MAX_SEQ_LENGTH

Troubleshooting

While working with UDA, you might encounter a few bumps on the road. Here are some troubleshooting tips:

• If memory errors arise, consider reducing the MAX_SEQ_LENGTH or decreasing the batch size.
• Run the script with fewer augmented copies if disk space is an issue.

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General Guidelines for Optimizing Hyperparameters

UDA is designed to operate effectively without excessive hyperparameter tuning. However, here are a few suggestions if you want to squeeze out even more performance:

• Set unsup_coeff to 1 for the weight on the unsupervised objective.
• Implement a lower learning rate as two loss terms will be computed.
• Experiment with batch sizes for the unsupervised objective to see what yields the best results.

Conclusion

With UDA, you can take your model’s performance from good to outstanding, all while using fewer resources. It stands as a testament to innovation in the world of artificial intelligence.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.