Mode Collapse Detection - Entrenar - Training & Optimization Library
Description: A comprehensive guide to building neural network training systems with autograd, optimizers, LoRA/QLoRA, and quantization using EXTREME TDD methodology

# Mode Collapse Detection

Mode collapse occurs when the generator produces limited variety.

## What is Mode Collapse?

- Generator ignores latent input
- All outputs look similar
- Discriminator can't distinguish (always ~0.5)
- Training becomes unstable

## Detection Methods

### Diversity Score

    #![allow(unused)]
    fn main() {
    let collapse_score = gan.detect_mode_collapse(num_samples);

    // collapse_score ∈ [0, 1]
    // 0 = perfect diversity
    // 1 = complete collapse (all identical)
    }

### Implementation

    #![allow(unused)]
    fn main() {
    pub fn detect_mode_collapse(&mut self, num_samples: usize) -> f32 {
        let latents = self.sample_latent(num_samples);
        let samples: Vec<Vec<u32>> = latents.iter()
            .map(|z| self.generator.generate(z))
            .collect();

        // Count unique sequences
        let unique: HashSet<_> = samples.iter().collect();
        let diversity = unique.len() as f32 / num_samples as f32;

        // Mode collapse score = 1 - diversity
        1.0 - diversity
    }
    }

## Prevention Strategies

### 1. Minibatch Discrimination

    #![allow(unused)]
    fn main() {
    fn minibatch_features(batch: &[Vec<u32>]) -> Vec<f32> {
        let features: Vec<Vec<f32>> = batch.iter()
            .map(|x| embed(x))
            .collect();

        // Compute pairwise distances
        let mut diversity_features = Vec::new();
        for i in 0..features.len() {
            let distances: Vec<f32> = features.iter()
                .map(|f| l2_distance(&features[i], f))
                .collect();
            diversity_features.extend(distances);
        }
        diversity_features
    }
    }

### 2. Historical Averaging

    #![allow(unused)]
    fn main() {
    fn historical_averaging_loss(
        current_params: &[f32],
        history: &VecDeque<Vec<f32>>,
    ) -> f32 {
        let avg: Vec<f32> = average_params(history);
        mse(current_params, &avg)
    }
    }

### 3. Unrolled GAN

    #![allow(unused)]
    fn main() {
    fn unrolled_generator_loss(
        gan: &mut CodeGan,
        unroll_steps: usize,
    ) -> f32 {
        // Save discriminator state
        let d_state = gan.discriminator.clone();

        // Unroll discriminator updates
        for _ in 0..unroll_steps {
            gan.train_discriminator_step(&real_batch);
        }

        // Compute generator loss against unrolled discriminator
        let g_loss = gan.generator_loss();

        // Restore discriminator
        gan.discriminator = d_state;

        g_loss
    }
    }

## Monitoring Dashboard

    #![allow(unused)]
    fn main() {
    // During training
    for step in 0..total_steps {
        let g_loss = gan.train_generator_step();
        let d_loss = gan.train_discriminator_step(&batch);

        if step % 100 == 0 {
            let collapse = gan.detect_mode_collapse(100);
            println!(
                "Step {}: G={:.4} D={:.4} Collapse={:.2}%",
                step, g_loss, d_loss, collapse * 100.0
            );

            if collapse > 0.8 {
                eprintln!("WARNING: Mode collapse detected!");
                // Consider: reduce LR, add noise, restart
            }
        }
    }
    }