Label-Guided Knowledge Distillation

• Label-guided knowledge distillation is a model compression technique that integrates ground-truth labels to correct and refine teacher predictions.
• Its methodology leverages approaches such as label revision, progressive distillation, and structure-level alignment to enhance training efficiency and robustness.
• Empirical studies show that these techniques improve performance across applications like speech recognition, object detection, and multilingual transfer.

Label-guided @@@@1@@@@ is a family of model compression and transfer learning techniques in which explicit incorporation of label information—rather than relying solely on teacher predictions—plays a central role in guiding the knowledge transfer from teacher to student. Modern formulations emphasize mechanisms that (i) correct, revise, or structure soft labels with reference to ground-truth annotations, (ii) use label structures to guide architecture adaptation or embedding alignment, or (iii) design progressive or label-conditioned transfer pipelines. Label-guided variants are motivated both by empirical evidence that teacher predictions alone are sometimes sub-optimal or insufficient, and by the need to ensure robust transfer in settings such as input-efficient modeling, weak supervision, or open-vocabulary recognition.

1. Conceptual Foundations

Label-guided knowledge distillation extends the classical formulation—where the student network minimizes a convex combination of the hard loss $\mathcal{L}_{hard}$ (cross-entropy with ground-truth $y$) and the soft loss $\mathcal{L}_{soft}$ (divergence between student and teacher logits or probabilities):

$$\mathcal{L} = \alpha \mathcal{L}_{hard} + (1-\alpha) \mathcal{L}_{soft}$$

by introducing explicit dependencies or constraints based on the label information. Techniques may involve linear combinations of teacher outputs and ground truth (label revision), selective supervision (data selection), or structure-level regularization. Progressive approaches, as in "Progressive Label Distillation" (Lin et al., 2019), link changes in label assignments to modifications in input semantics, aiming to avoid label misalignment when input dimensionality or content is altered.

Theoretical analysis identifies multiple roles for label guidance:

• Providing regularization by preventing overfitting to noisy teacher predictions and enforcing consistency with certified information.
• Disentangling class or instance relationships encoded in label hierarchies.
• Enabling compensation for unreliable, incomplete, or biased teacher outputs.

2. Methodological Approaches

Current label-guided knowledge distillation techniques can be classified along the following axes:

Approach	Mechanism	Example Paper
Label Revision	Linear interpolation p = β·p_t + (1–β)·y	(Lan et al., 3 Apr 2024)
Progressive Label Distillation	Teacher-generated labels on input-cropped data	(Lin et al., 2019)
Structure-level Distillation	CRF/sequence structure guided loss	(Wang et al., 2020)
Confidence-aware Multi-teacher	Label-guided weighting of teacher predictions	(Zhang et al., 2021)
Self-distillation w/ label input	Teacher input augmented by (noisy) labels	(Qiu et al., 18 Jul 2024)

Label Revision corrects potentially erroneous teacher soft labels by linearly combining them with one-hot ground-truth labels, as in (Lan et al., 3 Apr 2024):

$p = \beta \cdot p^t + (1-\beta) \cdot y$

The β parameter is controlled to enforce that the revised probability of the correct class exceeds that of any incorrect class.

Progressive Label Distillation (Lin et al., 2019) constructs a pipeline of teacher-student pairs, each distilling knowledge to successively smaller inputs. At each stage, the teacher network infers soft labels for dimension-reduced data (padded back to the teacher's input size), with the process repeated for further dimension reductions:

$$C^{(tgt)} = Distill_{int \to tgt}(D, Distill_{src \to int}(D, C^{(src)}))$$

Structure-level Knowledge Distillation (Wang et al., 2020) exploits global or local label sequence structure (e.g., in CRFs) by minimizing cross-entropy between student and teacher sequence probabilities or local posteriors derived from the sequence model's forward-backward algorithm.

3. Roles of Labels and Label Structures

Labels serve multiple roles across approaches:

• Supervisory Correction: Hard labels correct or constrain soft teacher assignments, limiting the propagation of teacher errors—implemented as direct interpolation or as a gating mechanism for which student samples should receive teacher supervision.
• Structural Regularization: Labels guide the design of new forms of distillation losses that capture sequence or hierarchical structure, such as in posterior or top-k sequence distillation (Wang et al., 2020).
• Guidance for Multi-teacher Distillation: Student models trained with multiple teachers can use ground-truth labels to assign per-sample, per-teacher confidence weights (Zhang et al., 2021).
• Input-Label Integration: Label information can be injected into the model's input (with noise) to facilitate robust denoising and privileged supervision, as in "Label Assisted Distillation" (Qiu et al., 18 Jul 2024).

These strategies exploit not only correctness but also contextual or semantic structures implicit in labels (e.g., class hierarchies or co-occurrence patterns).

4. Empirical Performance and Experimental Evidence

Empirical studies demonstrate that label-guided techniques improve performance, particularly in challenging or ill-posed conditions:

• Input-efficient speech recognition: Progressive label distillation improves accuracy from 12.03% (direct training on cropped input) to 89.22% (progressive distillation), while reducing computational cost by over 50% (Lin et al., 2019).
• Robustness to teacher error: Label revision combined with data selection yields up to 1.6% improvements in student accuracy over vanilla KD on CIFAR-100 and ImageNet, with minimal extra computational cost (Lan et al., 3 Apr 2024).
• Zero-shot transfer in multilingual tasks: Structure-level, label-guided distillation consistently outperforms emission-only approaches, and allows a single student to rival or surpass monolingual teacher ensembles in low-resource language transfer scenarios (Wang et al., 2020).
• Object detection: Self-distillation guided by label-derived object relations provides a 2.8% improvement in mean average precision on MS-COCO over standard teacher-based distillation, while reducing dedicated distillation training cost by over 50% (Zhang et al., 2021).
• Multi-label and structured settings: CAM- and label-embedding-based losses outperform classic logit or feature-based KD by directly linking label-wise activations to student outputs (Zhang et al., 2023, Yang et al., 2023).

5. Theoretical Perspectives

Analysis of bias–variance tradeoffs (Menon et al., 2020) and learnability guarantees for biased soft labels (Yuan et al., 2023) clarifies both the advantages and limitations of label-guided KD:

• Reliance on ground-truth guidance or bias correction reduces variance and mitigates the negative impact of miscalibrated teacher predictions, especially if the teacher’s class probabilities do not accurately estimate Bayes-optimal posteriors.
• The effectiveness of biased or “top-k” soft labels is quantified via unreliability degree $\Delta$ and ambiguity degree $\gamma$, with sufficient conditions for classifier-consistency and ERM learnability established as $\gamma < 1 - (\Delta / (1-\Delta))$ and $\Delta + \gamma < 1$ (Yuan et al., 2023).

This suggests that even biased or noisy teacher labels—provided the true label is contained within a carefully controlled subset—can yield effective gradient signals for the student.

6. Practical Implications and Broader Applications

Label-guided knowledge distillation enables multiple advances:

• Resource-Constrained Deployment: Student models trained with label-guidance can match or approach teacher performance with greatly reduced input requirements and model size, supporting edge and mobile scenarios (Lin et al., 2019).
• Robustness to Label or Teacher Noise: Dynamic revision of soft labels and selective student supervision shield against the negative influence of erroneous teacher predictions (Lan et al., 3 Apr 2024).
• Open-vocabulary Transfer and Multimodal Generalization: Label-guided techniques extend naturally to cross-modal and open-vocabulary recognition, bridging the gap between 2D vision-language teachers and 3D perception models through instance-level, label-consistent embedding alignment (Wu et al., 9 Oct 2025).
• Semantic Segmentation without Heavy Teachers or Extra Modalities: Incorporation of noisy label inputs into the teacher model, regularized by dual-path consistency, enables lightweight teachers to provide competitive distillation signals (Qiu et al., 18 Jul 2024).

A plausible implication is that as model architectures, data modalities, and label structures grow in complexity, label-guided approaches may be required to ensure that generalization, robustness, and efficiency targets are met without overfitting to the idiosyncrasies or errors of powerful teacher models.

7. Limitations and Research Directions

Label-guided knowledge distillation is not without unresolved questions:

• Optimal Combination Strategies: Hyperparameter tuning for combining soft and hard supervision remains heuristic, and the design of adaptive or data-driven combination rules is an area of ongoing research (Lan et al., 3 Apr 2024, Zhang et al., 2021).
• Data and Label Quality: The effectiveness of label-guided methods still depends on the fidelity of ground-truth labels, which may be noisy or incomplete in weakly-supervised or self-supervised settings.
• Scope of Applicability: While progressive and structure-level approaches have demonstrated success in domains such as speech, NLP, and vision, their generalization to other modalities (e.g., graph or time-series data) remains to be fully explored.
• Integration with Large Foundation Models: As large language and vision-LLMs become standard, engineering efficient, label-consistent transfer pathways for open-vocabulary, multilingual, or multimodal learners will be crucial (Sakai et al., 12 May 2025, Wu et al., 9 Oct 2025).

Continued development of theoretically-grounded, empirically robust label-guided distillation frameworks is likely to remain a major research focus, enabling more adaptable, efficient, and generalizable machine learning systems.