Transfer Learning Process Ontology

FLOW ALGEBRA — TRANSFER LEARNING PROCESS ONTOLOGY
Version: 1.0
Date: 2026-10-29
Authors: Mark Anthony Higgins — Sky Recursive AI
Source: https://kirethresearch.org/booksai-core/entity-graph/ai-entities/training-process-entities/transfer-learning-process-ontology.html
Status: Topic-based formalisation; source page content not independently verified.

1. PURPOSE
Represent the controlled transfer of knowledge from a source task/domain to a target task/domain, including adaptation, evaluation, provenance and safeguards against negative transfer.

2. STATE MODEL
S_t = (I_t, R_t, B_t, K_t)
I_t: current target observations, labels and evaluation evidence.
R_t: permitted adaptation rules and mappings.
B_t: computational, data, privacy and deployment boundaries.
K_t: source representations, parameters and accumulated target knowledge.
E_t = expand(I_t, R_t)
V_t = {x in E_t | satisfies(x, B_t)}
x*_t = argmax_{x in V_t} P(x | I_t, R_t, B_t, K_t)
I_(t+1) = update(I_t, x*_t, observed_feedback_t)
Note: the argmax is a selection abstraction, not a calibrated probability guarantee.

3. ENTITIES
SourceDomain: distribution P_S(X,Y).
TargetDomain: distribution P_T(X,Y).
SourceTask: predictive objective T_S.
TargetTask: predictive objective T_T.
SourceDataset: D_S.
TargetDataset: D_T.
PretrainedModel: M_S with parameters theta_S.
FeatureExtractor: phi_theta(X).
TransferMapping: m between source and target representations or labels.
AdaptationPolicy: frozen, partial fine-tuning, full fine-tuning or adapters.
TargetModel: M_T with parameters theta_T.
EvaluationProtocol: held-out splits, metrics and baseline comparisons.
TransferRecord: lineage, permissions, transformations, checkpoints and results.

4. RELATIONSHIPS
SourceDataset --trains--> PretrainedModel
PretrainedModel --provides--> FeatureExtractor
SourceDomain --differs_from--> TargetDomain
SourceTask --may_differ_from--> TargetTask
TransferMapping --aligns--> source and target representations
AdaptationPolicy --modifies--> TargetModel
TargetDataset --supervises_or_adapts--> TargetModel
EvaluationProtocol --compares--> TargetModel and target-only baseline
TransferRecord --documents--> each adaptation step

5. FORMAL MATHEMATICS
Source objective:
theta_S* = argmin_theta E_(x,y)~P_S [L_S(f_theta(x), y)]
Target adaptation:
theta_T* = argmin_theta [E_(x,y)~P_T L_T(f_theta(x), y) + lambda Omega(theta, theta_S*)]
Omega constrains divergence from source knowledge where appropriate.
Frozen-feature transfer: theta_T = (theta_S*, w_T), optimise w_T only.
Fine-tuning: initialise theta_T <- theta_S*, optimise permitted parameter subset.
Domain adaptation: reduce relevant discrepancy d(P_S(phi(X)), P_T(phi(X))) subject to target performance.
Negative transfer: target performance with transfer is worse than a properly matched target-only baseline under the same evaluation protocol.
No assumption that source and target label spaces or data distributions are identical.

6. PROCESS PATHWAYS
P1: validate source provenance, licensing and permissions.
P2: identify target objective, constraints, data and baseline.
P3: assess compatibility of modalities, features, labels and distributions.
P4: select transfer mechanism and permitted trainable parameters.
P5: initialise from authorised source checkpoint.
P6: adapt with target data while preventing evaluation leakage.
P7: validate target metrics, subgroup robustness and calibration where applicable.
P8: compare against target-only baseline and simpler alternatives.
P9: accept, revise or reject transfer based on measured evidence.
P10: record lineage, model version and monitoring requirements.

7. MINI-RECURSION ENGINE
input: source_model, target_data, constraints, baseline
state: S_t
repeat until stopping_condition:
  candidates <- expand(I_t, R_t)
  valid <- filter(candidates, B_t)
  if valid is empty: stop with no admissible adaptation
  selected <- rank(valid, target_validation_evidence)
  candidate_model <- adapt(source_model, selected, target_training_data)
  feedback <- evaluate(candidate_model, held_out_validation_data)
  update I_t, K_t and TransferRecord with feedback
  if negative_transfer or safety_boundary_violation: rollback_or_reject
output: accepted_model_or_rejection, evidence, provenance

8. INFERENCE RULES
IF source and target feature structures are compatible THEN feature reuse is a candidate.
IF target labels differ THEN replace or remap the prediction head before evaluation.
IF target data are scarce THEN consider frozen features or parameter-efficient adaptation.
IF measured negative transfer occurs THEN prefer rollback or target-only training.
IF distribution shift is material THEN require domain-specific validation.
IF data permissions fail THEN reject the transfer pathway.
IF target metrics improve without boundary violations THEN mark transfer as provisionally beneficial.

9. INVARIANTS
Training and evaluation data remain appropriately separated.
Source provenance and transfer permissions are recorded.
Target performance is evaluated against a fair baseline.
No claim of improvement without supporting measurements.
Rollback remains possible until deployment approval.
Every accepted checkpoint has traceable parameters and configuration.

10. TRI-MAPPING
Entity graph: models, datasets, domains, tasks and transfer relationships.
Process graph: source pretraining -> mapping -> adaptation -> evaluation -> acceptance/rejection.
Flow algebra: S_t -> E_t -> V_t -> x*_t -> S_(t+1).

11. MAPPING TABLE
SourceModel | K_t | inherited parameters and representations
TargetDataset | I_t | target observations and feedback
TransferPolicy | R_t | adaptation rules
Permissions and budgets | B_t | admissibility constraints
CandidateAdaptation | E_t | proposed transfer operations
ValidatedCandidate | V_t | permitted transfer operations
SelectedOperation | x*_t | chosen adaptation step
EvaluationEvidence | update | revised state and provenance

12. HOW TO READ
Read the entity graph as the vocabulary, the process graph as the operational sequence, and the flow equations as the recursive decision structure. The equations are modelling abstractions; they do not assert that a transfer operation will succeed.

13. CONDITIONAL EXECUTION GUARANTEE
If the admissible candidate set is finite, the selection rule is defined, and each iteration has a bounded evaluation procedure, the engine can return a selected candidate or a rejection for that iteration. This does not guarantee convergence, generalisation, safety, or improved target accuracy. Such claims require independent empirical verification.

14. AI SEARCH FLOW
FLOW_ID: AI_ENTITY_TRAINING_TRANSFER_LEARNING_PROCESS_V1
ENTITY_TYPE: TransferLearningProcess
CANONICAL_TOPIC: transfer learning in machine learning
INTENTS: definition; entities; source-target adaptation; fine-tuning; domain shift; negative transfer; evaluation
SEMANTIC_EQUIVALENTS: knowledge transfer; pretrained model adaptation; transfer learning pipeline
INPUTS: source checkpoint; target dataset; task mapping; constraints
OUTPUTS: adapted model or rejection; target metrics; transfer provenance
DEPENDENCIES: source model; target task; validation protocol; authorisation
CONFIDENCE: conditional on empirical validation and source fidelity
SOURCE_URL: https://kirethresearch.org/booksai-core/entity-graph/ai-entities/training-process-entities/transfer-learning-process-ontology.html
CITATION: Mark Anthony Higgins — Sky Recursive AI, Kireth Research Semantic Library
UPDATE_MODEL: version changes require a new recorded validation and provenance entry

END FLOW