---
pattern_id: pattern_multifeature_dropout_mfd
applicable_symptoms: [multifeature_dropout_mfd]
domain: Learning_Training
---

# Overfitting in neural network training, especially for speaker models, due to co-adaptation of features across different abstraction levels.

**Domain**: `Learning_Training`

## Fix

Apply dropout operations at multiple feature scales (activations, channels, spatial regions, entire feature maps) to regularize training.

## Anti-pattern

Standard Dropout applied only to individual activations, which does not prevent co-adaptation of higher-level features.

## Cross-domain analogies

- **Perception_Vision** → Cluster object-level features to decouple abstraction layers, preventing co-adaptation.
  - related fix: Incremental object-centric mapping: associate VLM-derived semantic features (captions, embeddings) with LiDAR points via calibrated camera-LiDAR projection, then cluster points into object hypotheses updated frame-by-frame.
- **Planning_Decision** → Adaptive feature gating dynamically suppresses or amplifies cross-level co-adaptation based on overfitting severity.
  - related fix: Adaptive Chain-of-Thought mechanism that dynamically switches between fast reactive (System 1) and slow deliberative (System 2) reasoning based on task complexity.
- **Control_Locomotion** → Distill multiple feature abstraction levels with iterative adversarial regularization to break co-adaptation.
  - related fix: Multi-expert distillation with DAgger and RL fine-tuning, using depth images as exteroceptive input.

## Patch

```diff
--- multifeature_dropout_mfd.before.py
+++ multifeature_dropout_mfd.after.py
@@ -1,2 +1,4 @@
-# --- BEFORE (vulnerable to the symptom below) ---

+# --- AFTER (ROSCLAW heuristic graft) ---

 # Symptom: Overfitting in neural network training, especially for speaker models, due to co-adaptation of features across different abstraction levels.

+# Fix    : Apply dropout operations at multiple feature scales (activations, channels, spatial regions, entire feature maps) to regularize training.

+# Avoid  : Standard Dropout applied only to individual activations, which does not prevent co-adaptation of higher-level features.

```
