Cross-Architecture Dataset Distillation Study

A comprehensive implementation and evaluation framework for testing cross-architecture generalization in dataset distillation methods. This repository provides systematic tools to answer three key research questions about multi-architecture dataset distillation.

🎯 Research Questions

RQ-1: How well does Progressive Dataset Distillation generalize across different neural network architectures (CNNs vs Transformers)?

RQ-2: How does the multi-architecture approach compare to existing cross-architecture generalization methods (Model Pool, MetaDD)?

RQ-3: What are the optimal design choices for multi-architecture dataset distillation (number of stages, architecture sampling, etc.)?

🏗️ Architecture Overview

📁 Project Structure
├── 📄 README.md                     # This comprehensive guide
├── 📄 requirements.txt              # Python dependencies  
├── 📄 validate_setup.py             # Environment validation
├── 📄 getting_started.py            # Interactive setup guide
├── 📄 run_experiments.py           # Main experiment runner
├── 📄 aggregate_results.py         # Results analysis system
├── 📂 configs/
│   └── 📄 experiment_configs.yaml  # All experiment configurations
│
├── 📂 Core Architectures
│   └── 📄 architectures.py         # SimpleCNN, SmallViT, ResNet8
│
├── 📂 Distillation Methods
│   ├── 📄 dd_baseline_17june.py         # Single-Step DD (your existing)
│   ├── 📄 pdd_baseline_17june.py        # Progressive DD (your existing)
│   ├── 📄 multi_arch_distill.py         # Multi-Architecture Progressive DD ⭐
│   ├── 📄 model_pool_distill.py         # Model Pool baseline
│   └── 📄 metadd_distill.py             # MetaDD-inspired baseline
│
├── 📂 Evaluation & Analysis
│   ├── 📄 transfer_train.py             # Cross-architecture evaluation
│   ├── 📄 eval_metrics.py               # Comprehensive metrics
│   └── 📄 plot_synthetic_grid.py        # Visualization utilities
│
└── 📂 results/                          # Generated results directory
    ├── 📂 dd_experiment/
    ├── 📂 pdd_experiment/
    ├── 📂 multi_arch_pdd/              # Your novel method results
    ├── 📂 model_pool_dd/
    ├── 📂 metadd_distill/
    ├── 📂 transfer_evaluation/
    └── 📂 final_analysis/               # Comprehensive comparison

🚀 Quick Start Guide

Step 1: Environment Setup

# Clone or download your research files
# Navigate to the project directory

# Create Python environment
python -m venv dd_env
source dd_env/bin/activate  # On Windows: dd_env\Scripts\activate

# Install dependencies
pip install -r requirements.txt

# Validate setup
python validate_setup.py

Step 2: Interactive Getting Started

# Run the interactive guide (recommended for first-time users)
python getting_started.py

This will walk you through the entire setup process and run your first experiment.

Step 3: Create Configuration File

The configuration system ensures reproducible experiments. Create configs/experiment_configs.yaml:

# Base Configuration
dataset:
  name: "MNIST"
  num_classes: 10
  images_per_class: 10
  batch_size: 256

computational_budget:
  total_gradient_computations: 3000

architectures:
  available: ["SimpleCNN", "SmallViT", "ResNet8"]

training:
  lr_synthetic: 0.0001
  lr_inner: 0.01
  device: "cpu"

# Add experiment-specific configurations...

Step 4: Run Complete Study

# Run all experiments for all research questions
python run_experiments.py --full_study

📊 Individual Experiment Commands

Your Novel Method (Multi-Architecture Progressive DD)

# Run your core contribution
python run_experiments.py --config multi_arch_pdd

# Or run directly with custom parameters
python multi_arch_distill.py --architectures SimpleCNN SmallViT \
    --arch_sampling random --num_stages 5 --save_dir ./results/custom

Baseline Methods for Comparison

# Single-step DD baseline
python run_experiments.py --config dd_baseline

# Progressive DD baseline  
python run_experiments.py --config pdd_baseline

# Model Pool baseline (for RQ-2)
python run_experiments.py --config model_pool_dd

# MetaDD-inspired baseline (for RQ-2)
python run_experiments.py --config metadd_distill

Cross-Architecture Evaluation (RQ-1)

# Run all baseline methods first
python run_experiments.py --run_all_baselines

# Then evaluate cross-architecture generalization
python run_experiments.py --transfer_only

# Or evaluate specific method on specific architecture
python transfer_train.py --dataset_path ./results/multi_arch_pdd/multi_arch_pdd_dataset.pt \
    --target_arch SmallViT --include_baseline --plot_curves

Hyperparameter Analysis (RQ-3)

# Systematic hyperparameter sweep
python run_experiments.py --sweep --method multi_arch_pdd

# This tests different values of:
# - Number of stages (1, 2, 5, 10)
# - Sampling strategies (random, all, alternating)
# - Architecture pool sizes
# - Feature alignment weights

🔬 Method Details

1. Multi-Architecture Progressive DD (Your Core Contribution)

This is your novel approach that directly addresses the cross-architecture generalization problem. Instead of distilling with a single architecture, you randomly sample from multiple architectures during the progressive distillation process.

Key Innovation:

# Traditional PDD uses single architecture
model = SimpleCNN()

# Your approach samples from architecture pool
arch_pool = ['SimpleCNN', 'SmallViT'] 
for outer_step in range(outer_steps):
    sampled_arch = random.choice(arch_pool)
    model = get_architecture(sampled_arch)
    # ... distillation with diverse architectures

Why This Works: By exposing the synthetic data to different architectural inductive biases during creation, the resulting dataset captures more generalizable features rather than architecture-specific artifacts.

2. Fair Comparison Framework

All methods use exactly 3000 gradient computations for fair comparison:

Method	Structure	Computations
Single-Step DD	3000 iterations × 1 step	3000
Progressive DD	5 stages × 200 outer × 3 inner	3000
Multi-Arch PDD	5 stages × 200 outer × 3 inner	3000
Model Pool	3000 iterations × 1 step	3000
MetaDD	3000 iterations × 1 step	3000

3. Architecture Specifications

The framework tests three fundamentally different architectures:

SimpleCNN (~50K parameters):

Local receptive fields through convolutions
Translation equivariance
Hierarchical feature learning

SmallViT (~45K parameters):

Global attention mechanisms
Explicit positional encoding
Patch-based processing

ResNet8 (~55K parameters):

Residual connections for gradient flow
Batch normalization
Skip connections

📈 Results Analysis

Automated Analysis Pipeline

The framework provides comprehensive automated analysis:

# Generate complete comparison analysis
python aggregate_results.py --results_dir ./results --output_dir ./analysis

# Creates publication-ready:
# - Cross-architecture performance heatmaps
# - Method comparison bar charts  
# - Generalization consistency analysis
# - Performance vs stability trade-offs
# - Statistical significance tests

Key Metrics for Each Research Question

RQ-1 Metrics (Cross-Architecture Generalization):

Average accuracy across target architectures
Generalization consistency: min_accuracy / max_accuracy * 100
Performance retention: synthetic_accuracy / real_accuracy * 100
Cross-architecture gap: |accuracy_CNN - accuracy_ViT|

RQ-2 Metrics (Method Comparison):

Performance ranking by mean accuracy
Stability ranking by accuracy variance
Efficiency: accuracy per gradient computation
Statistical significance tests between methods

RQ-3 Metrics (Design Choices):

Progressive vs single-step performance comparison
Architecture pool size impact analysis
Sampling strategy effectiveness evaluation
Convergence speed and stability analysis

🎯 Expected Results Structure

After running the complete study:

📂 results/
├── 📂 dd_experiment/
│   ├── dd_synthetic_dataset.pt         # Single-step DD results
│   └── dd_synthetic_images.png
│
├── 📂 multi_arch_pdd/                  # Your novel method ⭐
│   ├── multi_arch_pdd_dataset.pt       # Key results for RQ-1 & RQ-2
│   ├── multi_arch_pdd_results.json     
│   └── multi_arch_pdd_synthetic_images.png
│
├── 📂 transfer_evaluation/              # Cross-architecture results
│   ├── transfer_SimpleCNN_results.json  # RQ-1 evaluation data
│   ├── transfer_SmallViT_results.json
│   └── transfer_ResNet8_results.json
│
└── 📂 final_analysis/                   # Comprehensive comparison
    ├── research_findings_summary.txt    # Direct answers to all RQs
    ├── detailed_results.csv
    └── 📂 visualizations/
        ├── cross_architecture_heatmap.png
        ├── method_comparison.png
        └── generalization_consistency.png

🔧 Advanced Usage

Custom Experiments

# Test specific architecture combinations
python multi_arch_distill.py --architectures SimpleCNN SmallViT ResNet8 \
    --arch_sampling all --num_stages 10

# Compare sampling strategies
for strategy in random all alternating; do
    python multi_arch_distill.py --arch_sampling $strategy \
        --save_dir ./results/sampling_$strategy
done

# Test different stage counts (for RQ-3)
for stages in 1 2 5 10; do
    python multi_arch_distill.py --num_stages $stages \
        --save_dir ./results/stages_$stages
done

Analyzing Specific Research Questions

For RQ-1 (Cross-Architecture Generalization):

# Generate synthetic data with your method
python multi_arch_distill.py --architectures SimpleCNN SmallViT

# Test on all target architectures
for arch in SimpleCNN SmallViT ResNet8; do
    python transfer_train.py \
        --dataset_path ./results/multi_arch_pdd/multi_arch_pdd_dataset.pt \
        --target_arch $arch --include_baseline
done

# Analyze generalization gaps
python eval_metrics.py --transfer_results ./results/transfer_evaluation/

For RQ-2 (Method Comparison):

# Run all methods
python run_experiments.py --run_all_baselines

# Statistical comparison
python aggregate_results.py --results_dir ./results \
    --generate_significance_tests

For RQ-3 (Design Choices):

# Hyperparameter sweep
python run_experiments.py --sweep

# Analyze results
python aggregate_results.py --sweep_analysis

🐛 Troubleshooting

Common Issues and Solutions

Environment Setup Issues:

# Validate environment
python validate_setup.py --verbose

# Auto-fix common problems
python validate_setup.py --fix-issues

CUDA/GPU Issues (Since you're using CPU):

# Ensure CPU-only operation
export CUDA_VISIBLE_DEVICES=""
python run_experiments.py --full_study

Memory Issues:

# Reduce batch size if needed
python multi_arch_distill.py --batch_size 128

# Quick test with smaller dataset
python run_experiments.py --config quick_test

Missing Dependencies:

# Install specific missing packages
pip install torch torchvision matplotlib seaborn pandas numpy PyYAML tqdm

# Or reinstall everything
pip install -r requirements.txt --force-reinstall

📝 Research Methodology

Experimental Design Principles

This framework follows rigorous experimental design principles essential for publishable research:

Controlled Variables: All methods use identical computational budgets, dataset sizes, evaluation protocols, and random seeds.

Fair Comparison: The 3000 gradient computation budget ensures no method has an unfair advantage due to more optimization steps.

Statistical Rigor: Multiple random seeds, confidence intervals, and significance testing ensure reliable conclusions.

Reproducibility: Configuration-driven experiments with fixed seeds enable exact reproduction of results.

Addressing Each Research Question

RQ-1 Experimental Design: Your multi-architecture progressive method is compared against single-architecture baselines using identical synthetic dataset sizes. Cross-architecture transfer evaluation measures how well synthetic data from each method transfers to held-out architectures.

RQ-2 Experimental Design: Direct comparison with existing methods (Model Pool, MetaDD) using identical evaluation protocols. Statistical significance testing determines whether performance differences are meaningful.

RQ-3 Experimental Design: Systematic hyperparameter sweeps test the impact of key design choices while maintaining computational budget constraints. This identifies optimal configurations for practical deployment.

📚 Publication-Ready Results

The framework generates publication-ready outputs:

Tables: Detailed performance comparisons with statistical confidence intervals.

Figures: High-quality visualizations showing cross-architecture performance, method comparisons, and design choice impacts.

Statistical Analysis: Significance tests, effect sizes, and confidence intervals for all major claims.

Reproducibility Information: Complete experimental configurations and random seeds for replication.

🤝 Contributing New Methods

Adding New Distillation Methods

# Template for new methods
def run_new_method(args):
    # 1. Create synthetic dataset (100 images, 10 per class)
    synthetic_images, synthetic_labels = create_synthetic_dataset(...)
    
    # 2. Distillation loop (exactly 3000 gradient computations)
    for iteration in range(3000):
        # Your distillation logic here
        # Must update synthetic_images based on some loss
        pass
    
    # 3. Save results in consistent format
    torch.save({
        'synthetic_images': synthetic_images,
        'synthetic_labels': synthetic_labels,
        'method_name': 'Your Method',
        'args': args
    }, save_path)
    
    return synthetic_images, synthetic_labels

Adding New Architectures

# Add to architectures.py
class NewArchitecture(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        # Your architecture here
        
    def forward(self, x):
        # Forward pass
        return output

# Update the factory function
def get_architecture(arch_name):
    if arch_name == 'NewArchitecture':
        return NewArchitecture()
    # ... existing architectures

🎉 Getting Started Checklist

Before running your experiments:

✅ Environment setup completed (python validate_setup.py)
✅ Configuration file created (configs/experiment_configs.yaml)
✅ Dependencies installed (pip install -r requirements.txt)
✅ Test run successful (python getting_started.py)
✅ Sufficient disk space (~2GB for complete study)
✅ Expected runtime understanding (2-4 hours for full study)

🏆 Success Criteria

Your research will be successful when you can demonstrate:

For RQ-1: Clear evidence that your multi-architecture progressive method achieves better cross-architecture generalization than single-architecture baselines.

For RQ-2: Statistical comparison showing your method's performance relative to existing approaches like Model Pool and MetaDD.

For RQ-3: Identification of optimal design choices through systematic hyperparameter analysis.

📞 Next Steps

Run Initial Validation: python validate_setup.py
Follow Interactive Guide: python getting_started.py
Test Single Method: python run_experiments.py --config multi_arch_pdd
Run Complete Study: python run_experiments.py --full_study
Analyze Results: Check ./final_analysis/research_findings_summary.txt

Your comprehensive dataset distillation research framework is ready to generate the evidence needed to answer your research questions systematically and rigorously! 🚀

Name		Name	Last commit message	Last commit date
Latest commit History 1 Commit
configs		configs
core architectures		core architectures
data/MNIST/raw		data/MNIST/raw
distillation methods		distillation methods
evaluation & analysis		evaluation & analysis
results		results
README.md		README.md
aggregate_results.py		aggregate_results.py
dd_baseline.py		dd_baseline.py
real_dd_baseline.py		real_dd_baseline.py
requirements.txt		requirements.txt
run_experiments.py		run_experiments.py

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Cross-Architecture Dataset Distillation Study

🎯 Research Questions

🏗️ Architecture Overview

🚀 Quick Start Guide

Step 1: Environment Setup

Step 2: Interactive Getting Started

Step 3: Create Configuration File

Step 4: Run Complete Study

📊 Individual Experiment Commands

Your Novel Method (Multi-Architecture Progressive DD)

Baseline Methods for Comparison

Cross-Architecture Evaluation (RQ-1)

Hyperparameter Analysis (RQ-3)

🔬 Method Details

1. Multi-Architecture Progressive DD (Your Core Contribution)

2. Fair Comparison Framework

3. Architecture Specifications

📈 Results Analysis

Automated Analysis Pipeline

Key Metrics for Each Research Question

🎯 Expected Results Structure

🔧 Advanced Usage

Custom Experiments

Analyzing Specific Research Questions

🐛 Troubleshooting

Common Issues and Solutions

📝 Research Methodology

Experimental Design Principles

Addressing Each Research Question

📚 Publication-Ready Results

🤝 Contributing New Methods

Adding New Distillation Methods

Adding New Architectures

🎉 Getting Started Checklist

🏆 Success Criteria

📞 Next Steps

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