# Contrastive & dual encoders


📂 model-architectures

## MODEL ARCHITECTURES

# Contrastive & dual encoders

Encode two inputs independently (such as a query and document or image and caption) and train matching pairs to land near each other.

## MENTAL MODEL

Two readers meet in a shared coordinate system; they are fast because candidates do not interact until their vectors are compared.

## DATA FLOW

- Paired inputs A and B

- Separate or shared encoders

- Projected normalized vectors

- Similarity matrix

- Contrastive matching loss

## How it trains

Batch contrastive objectives reward paired examples and treat other examples as negatives. Negative quality, duplicate semantics, and temperature materially affect the learned space.

## How inference runs

Encode each side independently and compare vectors with dot product or cosine similarity. Precompute the large candidate side; optionally rerank top results with a cross-encoder.

## Strengths

- •
Scales retrieval to large corpora

- •
Aligns modalities without a joint decoder

- •
Enables zero-shot classification through label text in some settings

## Trade-offs

- •
Independent encoding misses fine-grained cross-input interactions

- •
False negatives can distort training

- •
Global similarity may ignore spatial, temporal, or compositional details

## Use it when

- Fast retrieval or matching is the first stage

- One side can be indexed offline

- Recall is followed by task-appropriate reranking when needed

## Avoid or challenge it when

- Every candidate needs deep token-to-token comparison

- The task depends on precise spatial relationships

- Raw similarity scores are assumed to be probabilities

## Illustrative published families

- • CLIP image–text dual encoder

- • Dense passage retrieval

- • Bi-encoder semantic search

## Commonly combines with

## PRIMARY REFERENCES

## Model Architectures

## Model Architectures