Skip to content

Track 14

Representation Reuse and Embedding Transfer

This track teaches the safer adaptation ladder for shifted or label-limited tasks: inspect the frozen encoder first, test it with a probe and retrieval checks, and only then decide whether a small fine-tune is worth the extra risk.

Open Transfer Topic Run Fast Examples Use The Study Plan

Primary Goal

Reuse Before You Retrain

The point is not to update every parameter by default. The point is to learn whether the frozen representation is already strong enough for the target task.

Best For

Shifted Tasks With Limited Labels

Use this track when you already know basic training loops and need a disciplined way to compare frozen reuse, retrieval checks, and light adaptation.

Exit Rule

Keep The Smallest Adaptation That Holds Up

You are done when you can defend whether the target task needs only a probe, a retrieval-backed reuse story, or a small fine-tune.

Use This Track When

  • you already know basic training loops and checkpointing
  • you have limited target labels or a moderate source-target shift
  • you need evidence that a frozen encoder is already useful before changing it
  • you want to compare reuse and adaptation on the same split without moving the whole pipeline at once

What This Track Is Training

This track trains one practical ladder:

  • freeze the encoder
  • test it with a linear probe
  • inspect nearest-neighbor retrieval
  • fine-tune only if the frozen story is clearly weak
  • keep the smallest adaptation that still survives the target split

That means the learner should be able to keep these explicit:

  • which parameters stayed frozen and which ones were allowed to move
  • whether probe, retrieval, and projection are telling the same representation story
  • whether any fine-tuning gain is large enough to justify the extra complexity
  • whether the label budget and target shift argue for reuse or adaptation

First Session

Use this order:

  1. Transfer and Fine-Tuning
  2. Vision and Text Encoders
  3. run academy/.venv/bin/python academy/examples/representation-reuse-recipes/frozen_probe_demo.py
  4. run academy/.venv/bin/python academy/examples/deep-learning-recipes/transfer_finetuning_demo.py
  5. write one short note on whether the safer next move is frozen reuse or a small target update

Full Track Loop

For the complete workflow:

  1. review the transfer and encoder topics before changing the model
  2. run the frozen-probe example and compare it with the transfer fine-tuning example
  3. install the lab requirements with academy/.venv/bin/python -m pip install -r academy/labs/representation-reuse-and-embedding-transfer/requirements.txt
  4. run the full lab with academy/.venv/bin/python academy/labs/representation-reuse-and-embedding-transfer/src/embedding_reuse_workflow.py
  5. finish the matching exercises in academy/exercises/representation-reuse-and-embedding-transfer/
  6. keep one short note with the probe result, retrieval evidence, fine-tune delta, and the smallest strategy you still trust

What To Inspect

By the end of the track, the learner should have inspected:

  • whether the frozen probe is already competitive on the target task
  • whether nearest neighbors make semantic sense on the examples that matter
  • whether the projection supports the story without being treated as proof
  • whether the fine-tuned run improves the same target split by enough to matter
  • whether the winning strategy is still the smaller acceptable move under limited labels

Common Failure Modes

  • fine-tuning before the frozen probe is understood
  • comparing target strategies on different splits or different encoder states
  • treating retrieval as decorative instead of as a representation check
  • trusting the projection picture more than the probe or validation evidence
  • keeping the fine-tuned encoder when frozen reuse already answers the task

Exit Standard

Before leaving this track, the learner should be able to:

  • freeze an encoder and train a probe on top of it
  • explain what nearest-neighbor retrieval says about the representation
  • compare frozen reuse against a small fine-tune on the same task
  • justify the smallest adaptation that still survives validation
  • say what evidence would make a larger update worth trying

That is enough to move into Optimization, Regularization, and PEFT or another advanced adaptation workflow.