DAM: Diffusion Activation Maximization for 3D Global Explanations

A method for generating global explanations of point clouds based on DDPM models. Usage:

Download dataset (ModelNet40, ShapeNet or other), put it in the /data folder.
Train the classifier to be explained by running train_classifier.py. For convenience, train_noised_classifier.py can be executed at the same time to train the noisified classifier, which improves performance during the explanation sampling process.
Train the PDT model by running train_PDT.py, which is the backbone of DDPM for generating highly perceptual explanations.
Run DAM_gen_one_sample.py or DAM_gen_batch.py to generate one or a batch of explanations, respectively.

Name		Name	Last commit message	Last commit date
Latest commit History 6 Commits
data		data
evaluation		evaluation
models		models
tools		tools
utils		utils
.gitignore		.gitignore
CD_eval.py		CD_eval.py
DAM_gen_batch.py		DAM_gen_batch.py
DAM_gen_one_sample.py		DAM_gen_one_sample.py
EMD_eval.py		EMD_eval.py
FID_eval.py		FID_eval.py
IGD_smoothness_eval.py		IGD_smoothness_eval.py
README.md		README.md
Sensitivity_IGD.py		Sensitivity_IGD.py
mIS_eval.py		mIS_eval.py
train_PDT.py		train_PDT.py
train_classifier.py		train_classifier.py
train_noised_classifier.py		train_noised_classifier.py
write_ply.py		write_ply.py

Explain3D/DAM