Specialised Heads

How to use a multi-headed architecture with specialized Heads to improve accuracy in tasks with multiple objectives.

What are specialized Heads?

The problem with the classical approach

The problem with a "universal head"

• Different signal nature — signals for direction and magnitude can have different natures and require different processing approaches
• Conflict of objectives — errors in one task can interfere with learning in another, creating a conflict between objectives
• Resource dilution — the model "spreads" its capacity thin and fails to reach the maximum on each subtask

The idea of specialized Heads

How it works

Step-by-step process:

• Process input features — the input features are processed by a shared part of the model (shared backbone), extracting universal patterns
• Specialize the Heads — each head receives the same features but is trained on its own task: Magnitude Head minimizes the magnitude error (e.g., MSE or Huber Loss); Direction Head minimizes the direction error (e.g., a sign-aware loss)
• Combine results — the Combiner receives both outputs and learns to weight them properly

Model architecture

Input Data
   ↓
Shared Backbone (universal patterns)
   ↓
[Magnitude Head — predicts magnitude]   [Direction Head — predicts direction]
   ↓
Combiner (final forecast)

Advantages of specialized Heads

• Independent optimization — each subtask is optimized independently, improving accuracy and reaching the maximum in each area
• Modularity — easy to add new tasks without rebuilding the whole model — just add a new Head
• Interpretability — easier to understand where the error lies: in direction or in magnitude
• Loss-function flexibility — different loss functions and weights can be applied per head

Example from financial forecasting

Implementation in code

Model structure:
class MultiHeadModel(nn.Module):
    def __init__(self):
        super().__init__()
        self.backbone = SharedBackbone()
        self.magnitude_head = MagnitudeHead()
        self.direction_head = DirectionHead()
        self.combiner = Combiner()

    def forward(self, x):
        features = self.backbone(x)
        magnitude = self.magnitude_head(features)
        direction = self.direction_head(features)
        final = self.combiner(magnitude, direction)
        return final, magnitude, direction

Loss functions for different Heads

• Magnitude Head — MSE, Huber Loss — Magnitude accuracy — How much the price will change
• Direction Head — Sign-Aware Loss — Direction correctness — Up or down
• Combiner — MSE, MAE — Final accuracy — Overall forecast
• Backbone — Not applied — Feature extraction — Universal patterns

When to use specialized Heads

• Multiple related tasks — when you need to solve several interconnected but qualitatively different tasks (direction + magnitude, classification + regression)
• Different error types — when different aspects of the task require different approaches to error minimization (absolute vs. relative accuracy)
• Interpretability requirements — when it is important to understand which part of the task the model fails on, for debugging and improvement
• Modular architecture — when you plan to add new tasks or modify existing ones without rebuilding the entire model

Practical recommendations

Implementation tips:

• Start with a simple architecture — use the same sizes for all Heads
• Tune the loss weights — different Heads may require different weights in the overall loss function
• Monitor each Head separately — track the performance of each subtask
• Test the Combiner — make sure the combination works correctly
• Consider ensembling — you can use several Heads of the same type

Key takeaways

Expert team analogy