Editing Semi-Supervised Learning (section)

== <span style="color: #FFFFFF;">Applying</span> ==
'''FixMatch implementation:'''
<syntaxhighlight lang="python">
import torch
import torch.nn.functional as F

def fixmatch_loss(model, labeled_x, labels, unlabeled_x_weak, unlabeled_x_strong,
                  threshold=0.95, lambda_u=1.0):
    # Supervised loss on labeled data
    logits_labeled = model(labeled_x)
    loss_supervised = F.cross_entropy(logits_labeled, labels)

    # Pseudo-label on weakly augmented unlabeled data
    with torch.no_grad():
        logits_weak = model(unlabeled_x_weak)
        probs_weak = F.softmax(logits_weak, dim=-1)
        max_probs, pseudo_labels = probs_weak.max(dim=-1)
        # Mask: only use predictions above confidence threshold
        mask = (max_probs >= threshold).float()

    # Consistency loss: predict pseudo-label on strongly augmented version
    logits_strong = model(unlabeled_x_strong)
    loss_unsupervised = (F.cross_entropy(logits_strong, pseudo_labels, reduction='none') * mask).mean()

    return loss_supervised + lambda_u * loss_unsupervised
</syntaxhighlight>

; Semi-supervised method selection
: '''Image classification''' → FixMatch, FlexMatch, FreeMatch (confidence threshold scheduling)
: '''NLP''' → UDA (Unsupervised Data Augmentation), pre-train then fine-tune (BERT approach)
: '''Graph data''' → Label propagation, Graph Convolutional Networks (GCN)
: '''Small labeled set (<100 samples)''' → Mean Teacher, MixMatch
: '''Production setting''' → Self-training with pseudo-labels (simple, scalable)
</div>

<div style="background-color: #8B4500; color: #FFFFFF; padding: 20px; border-radius: 8px; margin-bottom: 15px;">