This repository presents a differentiable K-Sparse AutoEncoder implementation that addresses the fundamental non-differentiability challenge in sparse representation learning. Our approach enables gradient-based training while maintaining strict sparsity constraints through a novel masked gradient flow mechanism. The implementation demonstrates superior reconstruction quality with reduced computational overhead compared to traditional sparse autoencoders.

Figure 1: K-Sparse AutoEncoder architecture with differentiable sparse layer implementation

Figure 2: Mathematical foundations including sparse activation functions, loss components, gradient flow, and sparsity patterns

The K-Sparse AutoEncoder enforces sparsity through top-k selection:

f_sparse(x) = top_k(f_encoder(x))

Where:

• f_encoder: R^n → R^m is the encoder function
• top_k(·) selects the k largest activations and zeros others
• Gradients flow only through active neurons via learned masks

Our implementation solves the non-differentiability of top-k selection by:

1. Forward Pass: Compute binary masks for top-k activations
2. Backward Pass: Route gradients through stored masks
3. Gradient Flow: Preserve sparsity while enabling gradient-based optimization

Figure 3: Comparative analysis showing method performance, scalability, memory efficiency, and convergence characteristics

Figure 4: High-quality sparsity analysis with properly trained models showing clear digit reconstructions, quality metrics, and compression efficiency across different k values

Figure 5: Detailed reconstruction comparison showing original MNIST digits and their high-quality reconstructions across all sparsity levels. Each pair shows Original | Reconstructed with clear digit recognition.

Table 1: Quantitative reconstruction quality metrics for different sparsity levels (properly trained models)

• Differentiable Sparse Layers: Gradient flow through top-k selection
• Multiple Activation Functions: Sigmoid, ReLU, Tanh, Leaky ReLU, ELU, Swish, GELU
• Advanced Optimizers: Adam, RMSprop, AdaGrad with sparse-aware variants
• Configurable Loss Functions: MSE, AuxK, Diversity, Comprehensive loss
• Model Persistence: Complete save/load with metadata and checksums

• Curriculum Learning: Progressive sparsity training
• Dead Neuron Detection: Automatic reset mechanisms
• Benchmarking Suite: Performance, quality, and scalability analysis
• Visualization Tools: Training progress, architecture diagrams, sparsity patterns
• Configuration Management: YAML/JSON configuration files

pip install numpy matplotlib seaborn scipy scikit-learn

from layers.sparse_layer import SparseLayer
from layers.linear_layer import LinearLayer
from nets.fcnn import FCNeuralNet
from utilis.activations import sigmoid_function

# Create K-Sparse AutoEncoder
encoder = SparseLayer("encoder", 784, 100, sigmoid_function, num_k_sparse=25)
decoder = LinearLayer("decoder", 100, 784, sigmoid_function)
model = FCNeuralNet([encoder, decoder])

# Train model
history = model.train(X_train, X_train, epochs=100, learning_rate=0.1)

# Generate predictions
reconstructions = model.predict(X_test)

from utilis.config import ConfigManager
from utilis.optimizers import OptimizerFactory, OptimizerType
from utilis.benchmarking import BenchmarkSuite

# Configuration management
config = ConfigManager()
config.load_config("config/experiment.yaml")

# Advanced optimization
optimizer = OptimizerFactory.create_optimizer(OptimizerType.ADAM, learning_rate=0.001)

# Comprehensive benchmarking
benchmark = BenchmarkSuite()
results = benchmark.run_comprehensive_benchmark(models, data, configs)

K-Sparse-AutoEncoder/
├── layers/                    # Neural network layers
│   ├── linear_layer.py        # Dense layer implementation
│   ├── sparse_layer.py        # K-sparse layer with differentiability
│   └── improved_sparse_layer.py # Advanced sparse layer features
├── nets/                      # Network architectures
│   ├── fcnn.py               # Fully connected neural network
│   └── improved_fcnn.py      # Enhanced network with advanced features
├── utilis/                    # Utility modules
│   ├── activations.py        # Activation functions
│   ├── optimizers.py         # Advanced optimization algorithms
│   ├── loss_functions.py     # Comprehensive loss functions
│   ├── benchmarking.py       # Performance evaluation suite
│   ├── visualization.py      # Scientific visualization tools
│   └── config.py             # Configuration management
├── tests/                     # Comprehensive test suite
├── images/                    # Generated figures and visualizations
└── demos/                     # Demonstration scripts

• Problem: Top-k selection is non-differentiable
• Solution: Masked gradient flow preserving sparsity
• Impact: Enables gradient-based training of sparse autoencoders

• Basic MSE: Standard reconstruction loss
• AuxK Loss: Auxiliary sparsity regularization
• Diversity Loss: Feature decorrelation
• Comprehensive Loss: Multi-objective optimization

• Curriculum Learning: Progressive sparsity scheduling
• Dead Neuron Detection: Automatic neuron reset
• Sparse-Aware Optimizers: Efficient sparse gradient updates

• Reconstruction Quality: MSE, PSNR, SSIM
• Sparsity Analysis: Compression ratio, active neuron statistics
• Computational Efficiency: Training time, memory usage
• Convergence Analysis: Loss curves, stability metrics

# Run comprehensive benchmarks
benchmark_suite = BenchmarkSuite("benchmarks/")
results = benchmark_suite.run_comprehensive_benchmark(
    models={'k_sparse': model},
    data={'test': test_data},
    configs={'default': config}
)

The implementation includes comprehensive testing:

• Unit Tests: 63 tests covering all components
• Integration Tests: End-to-end workflow validation
• Performance Tests: Benchmarking and regression testing
• Numerical Stability: Gradient flow verification

# Run test suite
python -m pytest tests/ -v

# Run specific test categories
python -m pytest tests/layers/ -v
python -m pytest tests/nets/ -v

• Sparse Representation Learning: Interpretable feature extraction
• Dimensionality Reduction: Efficient data compression
• Anomaly Detection: Sparse reconstruction-based detection
• Feature Selection: Automatic feature importance learning

• Image Compression: Lossy compression with quality control
• Data Preprocessing: Noise reduction and feature extraction
• Transfer Learning: Sparse feature representations
• Model Compression: Neural network pruning

• Learnable Sparsity Patterns: Adaptive k-selection
• Multi-Resolution Sparsity: Hierarchical sparse representations
• Attention-Based Sparsity: Content-aware sparse selection
• Variational Sparse Autoencoders: Probabilistic sparse representations

• GPU Acceleration: CUDA implementation for large-scale training
• Distributed Training: Multi-GPU and multi-node support
• Model Quantization: Reduced precision sparse representations
• Real-time Inference: Optimized deployment pipeline

If you use this implementation in your research, please cite:

@misc{ksparse_autoencoder_2024,
  title={K-Sparse AutoEncoder: A Differentiable Sparse Representation Learning Framework},
  author={Contributors},
  year={2024},
  url={https://github.com/snooky23/K-Sparse-AutoEncoder}
}

We welcome contributions! Please see our contribution guidelines for details.

# Clone repository
git clone https://github.com/snooky23/K-Sparse-AutoEncoder.git
cd K-Sparse-AutoEncoder

# Install development dependencies
pip install -r requirements-dev.txt

# Run tests
python -m pytest tests/ -v

This project is licensed under the MIT License - see the LICENSE file for details.

• Original K-Sparse AutoEncoder concept and implementation
• MNIST dataset from Yann LeCun et al.
• Scientific visualization inspired by matplotlib and seaborn communities
• Testing framework built on pytest

This implementation represents a significant advancement in sparse representation learning, providing a robust, differentiable framework for research and industrial applications.