SCAN: Visual Explanations with Self-Confidence and Analysis Networks (PyTorch Version)

Overview

This repository contains the PyTorch implementation of SCAN (Self-Confidence and Analysis Networks), a novel method for providing detailed visual explanations in computer vision models. SCAN leverages encoded representations and Self-Confidence Maps to highlight important regions, offering more detailed insights than existing methods like Explainability, Rollout, GradCAM, GradCAM++, and LayerCAM.

Repository Contents

• SCAN.py: The main PyTorch implementation file for SCAN.
• SCAN_Example_Training_PyTorch.ipynb: Jupyter notebook for training the SCAN model with PyTorch.

Files and Their Purpose

1. SCAN.py: Contains the core SCAN implementation for PyTorch, including:

   • ResidualModule: Residual block for the decoder
   • TransformerBlock: Transformer block for transformer-based decoders
   • ConvolutionalDecoder: Decoder for CNN-based models
   • TransformerDecoder: Decoder for transformer-based models (ViT, etc.)
   • SCAN: Main class for training and inference

2. SCAN_Example_Training_PyTorch.ipynb: Notebook for training the SCAN model on a specified dataset. This notebook includes:

   • Model loading and configuration
   • Dataset preparation
   • Training the decoder
   • Generating visual explanations
   • Visualization utilities

Installation

pip install -r requirements.txt

Requirements

• Python 3.8+
• PyTorch >= 2.0.0
• torchvision >= 0.15.0
• numpy >= 1.24.0
• matplotlib >= 3.6.0
• tqdm >= 4.64.0

Usage

Quick Start Example

import torch
from torchvision.models import resnet50, ResNet50_Weights
from SCAN import SCAN

# 1. Load target model
target_model = resnet50(weights=ResNet50_Weights.IMAGENET1K_V2)

# 2. Define preprocessing function
def preprocess_input(x):
    x = x / 255.0
    mean = torch.tensor([0.485, 0.456, 0.406], device=x.device).view(1, 3, 1, 1)
    std = torch.tensor([0.229, 0.224, 0.225], device=x.device).view(1, 3, 1, 1)
    return (x - mean) / std

# 3. Initialize SCAN
scanner = SCAN(
    target_model=target_model,
    target_layer='layer4',  # Use layer name
    image_size=(224, 224),
    use_gradient_mask=True,
    num_classes=1000
)

# 4. Set preprocessing and datasets
scanner.set_preprocess(preprocess_input)\
       .set_dataset(train_loader, use_augmentation=(70, 100))\
       .set_validation_dataset(valid_loader)\
       .generate_decoder(is_Transformer=False)\
       .compile(loss_alpha=4.0, learning_rate=1e-3)

# 5. Train
scanner.fit(epochs=5)

# 6. Generate visual explanation
confidence_map, reconstructed_image = scanner(image, percentile=95)

Key Differences from TensorFlow Version

Feature	TensorFlow	PyTorch
Target Layer	Layer index (int)	Layer name (str) or module reference
Dataset	tf.data.Dataset	torch.utils.data.DataLoader
Preprocessing	Function that takes tensor	Function that takes tensor
Image Format	(N, H, W, C)	(N, C, H, W)
Output	(H, W) confidence, (H, W, C) image	(H, W) confidence, (H, W, C) image

Target Layer Selection

For ResNet models:

• 'layer4': Last convolutional block (recommended)
• 'layer3': Earlier features (more detailed but noisier)
• 'layer4.2.conv3': Specific conv layer

For Vision Transformer (ViT):

scanner.generate_decoder(is_Transformer=True)

Saving and Loading

# Save trained decoder
scanner.save_decoder('scan_decoder.pt')

# Load decoder
scanner.load_decoder('scan_decoder.pt')

API Reference

SCAN Class

SCAN(
    target_model,        # PyTorch model to analyze
    target_layer,        # Layer name (str), module reference, or index (int)
    image_size=(224, 224),  # Input image size
    use_gradient_mask=True, # Use gradient masking
    decoder_model=None,  # Pre-trained decoder (optional)
    device=None,         # 'cuda' or 'cpu' (auto-detected if None)
    num_classes=1000     # Number of classes
)

Methods

• .set_preprocess(func): Set preprocessing function
• .set_dataset(dataloader, use_augmentation=(70, 100)): Set training data
• .set_validation_dataset(dataloader): Set validation data
• .generate_decoder(is_Transformer=False, ch_per_lv=[192, 256, 384, 512]): Create decoder model
• .compile(loss_alpha=4.0, optimizer_class=Adam, learning_rate=1e-3): Compile
• .fit(epochs=2, verbose=True): Train decoder
• .__call__(image, class_idx=None, percentile=95): Generate explanation

License

This project is licensed under the CC-BY-NC-SA license. See the LICENSE file for details.

Contact

For any questions or issues, please open an issue on the GitHub repository.

Citation

BibTex

@ARTICLE{Lee2026SCAN,
  author={Lee, Gwanghee and Jeong, Sungyoon and Jhang, Kyoungson},
  journal={IEEE Transactions on Artificial Intelligence}, 
  title={SCAN: Visual Explanations with Self-Confidence and Analysis Networks}, 
  year={2026},
  volume={},
  number={},
  pages={1-14},
  keywords={Image reconstruction;Visualization;Feature extraction;Transformers;Computational modeling;Semantics;Mathematical models;Analytical models;Predictive models;Decision making;Deep Learning;Computer Vision;Explainable AI;Visual Explanations},
  doi={10.1109/TAI.2026.3665492}}

APA

G. Lee, S. Jeong and K. Jhang, "SCAN: Visual Explanations with Self-Confidence and Analysis Networks," in IEEE Transactions on Artificial Intelligence, doi: 10.1109/TAI.2026.3665492.
keywords: {Image reconstruction;Visualization;Feature extraction;Transformers;Computational modeling;Semantics;Mathematical models;Analytical models;Predictive models;Decision making;Deep Learning;Computer Vision;Explainable AI;Visual Explanations},