This repository provides complementary code for the Targeted Dropout paper, enabling researchers and practitioners to implement and experiment with a novel dropout technique for neural networks. The primary benefit is improved model generalization and robustness through more efficient regularization.

How It Works

Targeted Dropout introduces a method to selectively drop units based on their importance, aiming to improve generalization and reduce overfitting. The implementation likely involves modifications to standard dropout layers to incorporate this targeted selection mechanism, potentially leading to more efficient training and better performance on downstream tasks.

Quick Start & Requirements

• Primary install / run command: python -m TD.train --hparams=resnet_default
• Prerequisites: Python 3, Tensorflow 1.8.
• The project supports different environments via the --env flag (local, gcp, tpu).
• Hparams can be specified or overridden using --hparams and --hparam_override flags.

Highlighted Details

• Code supports training and pruning models.
• Hparam sets like resnet_default are available for quick experimentation.
• Environment flags allow for flexible deployment on local machines, GCP, or TPUs.

Maintenance & Community

No specific community channels or maintenance details are provided in the README.

Licensing & Compatibility

The license is not specified in the README.

Limitations & Caveats

The project requires Tensorflow 1.8, which is an older version and may present compatibility challenges with modern Python environments or other libraries.