Class CurriculumLearner<T, TInput, TOutput>

Namespace

AiDotNet.CurriculumLearning

Assembly

AiDotNet.dll

Main orchestrator for curriculum learning that coordinates difficulty estimation, scheduling, and model training.

public class CurriculumLearner<T, TInput, TOutput> : ICurriculumLearner<T, TInput, TOutput>

Type Parameters

T

The numeric type used for calculations.

TInput

The type of input features.

TOutput

The type of output labels.

Inheritance

object

CurriculumLearner<T, TInput, TOutput>

Implements

ICurriculumLearner<T, TInput, TOutput>

Inherited Members

object.Equals(object)

object.Equals(object, object)

object.GetHashCode()

object.GetType()

object.MemberwiseClone()

object.ReferenceEquals(object, object)

object.ToString()

Remarks

For Beginners: Curriculum learning is a training strategy that presents samples from easy to hard, mimicking how humans learn. This class coordinates the entire process:

1. Estimates difficulty of each training sample
2. Sorts samples from easy to hard
3. Trains the model on progressively harder samples
4. Monitors progress and can stop early if model stops improving

Key Components:

• DifficultyEstimator: Measures how hard each sample is
• Scheduler: Controls when to introduce harder samples
• Config: Settings for training (epochs, batch size, early stopping)

Example Usage:

var learner = new CurriculumLearner<double, Vector<double>, double>(
    model,
    config,
    new LossBasedDifficultyEstimator<double, Vector<double>, double>());

var result = learner.Train(trainingData, validationData);

References:

• Bengio et al. "Curriculum Learning" (ICML 2009)
• Soviany et al. "Curriculum Learning: A Survey" (IJCV 2022)

Constructors

CurriculumLearner(IFullModel<T, TInput, TOutput>, ICurriculumLearnerConfig<T>, IDifficultyEstimator<T, TInput, TOutput>, ICurriculumScheduler<T>?)

Initializes a new instance of the CurriculumLearner<T, TInput, TOutput> class.

public CurriculumLearner(IFullModel<T, TInput, TOutput> baseModel, ICurriculumLearnerConfig<T> config, IDifficultyEstimator<T, TInput, TOutput> difficultyEstimator, ICurriculumScheduler<T>? scheduler = null)

Parameters

baseModel IFullModel<T, TInput, TOutput>

The model to train.

config ICurriculumLearnerConfig<T>

Configuration for curriculum learning.

difficultyEstimator IDifficultyEstimator<T, TInput, TOutput>

Estimator for sample difficulties.

scheduler ICurriculumScheduler<T>

Optional custom scheduler. If null, created based on config.

Properties

BaseModel

Gets the underlying model being trained.

public IFullModel<T, TInput, TOutput> BaseModel { get; }

Property Value

IFullModel<T, TInput, TOutput>

Config

Gets the configuration for the curriculum learner.

public ICurriculumLearnerConfig<T> Config { get; }

Property Value

ICurriculumLearnerConfig<T>

CurrentEpoch

Gets the current epoch number.

public int CurrentEpoch { get; }

Property Value

int

CurrentPhase

Gets the current training phase (0-1, where 1 means all samples are available).

public T CurrentPhase { get; }

Property Value

T

DifficultyEstimator

Gets the difficulty estimator used to rank samples.

public IDifficultyEstimator<T, TInput, TOutput> DifficultyEstimator { get; }

Property Value

IDifficultyEstimator<T, TInput, TOutput>

IsTraining

Gets whether training is currently in progress.

public bool IsTraining { get; }

Property Value

bool

Scheduler

Gets the curriculum scheduler that controls training progression.

public ICurriculumScheduler<T> Scheduler { get; }

Property Value

ICurriculumScheduler<T>

Methods

AdvancePhase()

Advances the curriculum to the next phase.

public bool AdvancePhase()

Returns

bool

True if advanced, false if already at final phase.

EstimateDifficulties(IDataset<T, TInput, TOutput>)

Estimates difficulty scores for all samples in a dataset.

public Vector<T> EstimateDifficulties(IDataset<T, TInput, TOutput> dataset)

Parameters

dataset IDataset<T, TInput, TOutput>

The dataset to estimate difficulties for.

Returns

Vector<T>

Vector of difficulty scores (higher = harder).

GetCurrentCurriculumIndices(Vector<T>)

Gets the indices of samples available at the current curriculum phase.

public int[] GetCurrentCurriculumIndices(Vector<T> allDifficulties)

Parameters

allDifficulties Vector<T>

Difficulty scores for all samples.

Returns

int[]

Indices of samples available for training at current phase.

GetPhaseHistory()

Gets the training history.

public IReadOnlyList<CurriculumPhaseResult<T>> GetPhaseHistory()

Returns

IReadOnlyList<CurriculumPhaseResult<T>>

List of results from each curriculum phase.

Load(string)

Loads the curriculum learner state.

public void Load(string path)

Parameters

path string

Path to load the state from.

ResetCurriculum()

Resets the curriculum to the initial phase.

public void ResetCurriculum()

Save(string)

Saves the curriculum learner state.

public void Save(string path)

Parameters

path string

Path to save the state.

Train(IDataset<T, TInput, TOutput>)

Trains the model using curriculum learning.

public CurriculumLearningResult<T> Train(IDataset<T, TInput, TOutput> trainingData)

Parameters

trainingData IDataset<T, TInput, TOutput>

The full training dataset.

Returns

CurriculumLearningResult<T>

Result containing training metrics and curriculum progression.

Remarks

This method will: 1. Estimate difficulty for all samples 2. Sort samples by difficulty (easy to hard) 3. Train in phases, gradually introducing harder samples 4. Track curriculum progression and model performance

Train(IDataset<T, TInput, TOutput>, IDataset<T, TInput, TOutput>?)

Trains the model with a validation set for monitoring.

public CurriculumLearningResult<T> Train(IDataset<T, TInput, TOutput> trainingData, IDataset<T, TInput, TOutput>? validationData)

Parameters

trainingData IDataset<T, TInput, TOutput>

The full training dataset.

validationData IDataset<T, TInput, TOutput>

The validation dataset.

Returns

CurriculumLearningResult<T>

Result containing training metrics and curriculum progression.

TrainWithDifficulty(IDataset<T, TInput, TOutput>, Vector<T>)

Trains with pre-computed difficulty scores.

public CurriculumLearningResult<T> TrainWithDifficulty(IDataset<T, TInput, TOutput> trainingData, Vector<T> difficultyScores)

Parameters

trainingData IDataset<T, TInput, TOutput>

The full training dataset.

difficultyScores Vector<T>

Pre-computed difficulty scores for each sample.

Returns

CurriculumLearningResult<T>

Result containing training metrics and curriculum progression.

Remarks

Use this method when: - Difficulty scores are computed externally (e.g., by domain experts) - You want to reuse difficulty scores across training runs - The difficulty estimation is too expensive to repeat

Events

PhaseCompleted

Event raised when a curriculum phase completes.

public event EventHandler<CurriculumPhaseCompletedEventArgs<T>>? PhaseCompleted

Event Type

EventHandler<CurriculumPhaseCompletedEventArgs<T>>

PhaseStarted

Event raised when a curriculum phase starts.

public event EventHandler<CurriculumPhaseEventArgs<T>>? PhaseStarted

Event Type

EventHandler<CurriculumPhaseEventArgs<T>>

TrainingCompleted

Event raised when training completes.

public event EventHandler<CurriculumTrainingCompletedEventArgs<T>>? TrainingCompleted

Event Type

EventHandler<CurriculumTrainingCompletedEventArgs<T>>