This is a clean implementation of CNN-LSX, a framework for training neural networks with high-quality saliency explanations.

CNN-LSX implements a novel approach for training neural networks that not only make accurate predictions but also produce meaningful explanations for those predictions. It uses a learner-critic framework:

• Learner: A neural network that makes predictions and generates explanations
• Critic: A separate network that evaluates the quality of those explanations

• Multiple explanation methods (input × gradient, integrated gradients, GradCAM)
• Flexible training pipeline with multiple stages (pretraining, joint training, finetuning)
• Support for different datasets (MNIST, DecoyMNIST, ColorMNIST)
• Comprehensive evaluation metrics for explanation quality

git clone https://github.com/yourusername/cnn-lsx.git
cd cnn-lsx
pip install -r requirements.txt

# Train on MNIST with default parameters
python main.py --dataset mnist --training_mode pretrain_and_joint_and_finetuning

# Train on a small portion of the data
python main.py --dataset mnist --few_shot_train_percent 0.02 --training_mode pretrain_and_joint_and_finetuning

• Classification only: --training_mode only_classification
• Joint training only: --training_mode joint
• Pretrain then joint train: --training_mode pretrain_and_joint
• Complete pipeline: --training_mode pretrain_and_joint_and_finetuning
• Finetuning only: --training_mode finetuning --model_pt path/to/model.pt
• Testing a model: --training_mode test --model_pt path/to/model.pt

Control the balance between classification accuracy and explanation quality:

python main.py --classification_loss_weight 1 --explanation_loss_weight 100 --explanation_loss_weight_finetune 100

1. Pretraining Phase: The learner is trained for classification only.
2. Joint Training Phase: The learner is trained to optimize both classification performance and explanation quality, as judged by the critic.
3. Finetuning Phase: The model is refined to further improve explanation quality.

Training progress and model evaluations are logged to TensorBoard:

tensorboard --logdir runs

For more information about CNN-LSX, including the theoretical background and implementation details, please refer to the documentation in the docs/ directory.