Classroom-Inspired Multi-Mentor Distillation with Adaptive Learning Strategies

## Abstract

Abstract We propose ClassroomKD , a novel multi-mentor knowledge distillation framework inspired by classroom environments to enhance knowledge transfer between student and multiple mentors. Unlike traditional methods that rely on fixed mentor-student relationships, our framework dynamically selects and adapts the teaching strategies of diverse mentors based on their effectiveness for each data sample. ClassroomKD comprises two main modules: the Knowledge Filtering (KF) Module and the Mentoring Module. The KF Module dynamically ranks mentors based on their performance for each input, activating only high-quality mentors to minimize error accumulation and prevent information loss. The Mentoring Module adjusts the distillation strategy by tuning each mentor’s influence according to the performance gap between the student and mentors, effectively modulating the learning pace. Extensive experiments on image classification (CIFAR-100 and ImageNet) and 2D human pose estimation (COCO Keypoints and MPII Human Pose) demonstrate that ClassroomKD significantly outperforms existing knowledge distillation methods. Our results highlight that a dynamic and adaptive approach to mentor selection and guidance leads to more effective knowledge transfer, paving the way for enhanced model performance through distillation.

## 1 Introduction

Knowledge distillation (KD) (Hinton et al., ) is a widely adopted model compression technique in deep learning, where a smaller, more efficient student model learns to replicate the behavior of a larger, more complex teacher model. While traditional KD methods typically employ a single teacher, multi-teacher (or multi-mentor) distillation has been proposed to further enhance performance by leveraging an ensemble of teachers (You et al., ). This setup is expected to provide richer and more diverse knowledge, improving the student’s generalization and robustness. We use the term mentor to describe all networks involved in teaching the student, regardless of their size or role.

Figure: Figure 1: (a) DML: Peer models learn from each other without a hierarchical teacher structure. (b) TAKD: A sequential mentor-student hierarchy with large-to-small knowledge transfer. (c) DGKD: Each mentor teaches all smaller models. (d) ClassroomKD: Our proposed method dynamically selects mentors for each data sample based on the current input and ranks them using the Knowledge Filtering Module. (e) Adaptive Mentoring: The Mentoring Module adjusts teaching strategies according to dynamic rankings, ensuring optimal knowledge transfer.
Refer to caption: x1.png

Despite its potential benefits, multi-mentor distillation faces several significant challenges:

Large Capacity Gap: Employing multiple large mentors can create a substantial capacity gap between the collective representation power of the mentors and that of the student. This gap can hinder the student’s ability to effectively mimic the combined knowledge of the mentors, leading to suboptimal learning outcomes. To bridge this gap, some works (Mirzadeh et al., ; Son et al., ) have introduced intermediate-sized mentors alongside a large teacher. However, smaller mentors may be less effective, potentially introducing additional errors into the student’s knowledge.

Error Accumulation: The lower performance of smaller mentors can contribute to cumulative errors in the distillation process. This is particularly problematic in sequential distillation frameworks like TAKD (Figure 1(b)), where each mentor teaches only the subsequent smaller model. Such setups can lead to an "error avalanche," where inaccuracies from lower-performing mentors degrade the student’s performance (Son et al., ). Although DGKD (Figure 1(c)) attempts to mitigate this by allowing each mentor to teach all smaller models and randomly dropping some mentors during training, these strategies can result in valuable information loss and reduced learning efficiency.

Lack of Dynamic Adaptation: The performance gap between the student and its mentors is not static; it evolves throughout training. Current methods do not adequately address these dynamic scenarios, limiting the effectiveness of multi-mentor distillation (Hao et al., ). Without an adaptive strategy, the potential benefits of multi-mentor distillation are not fully realized.

Observing that (1) a mentor’s performance varies across different data samples, (2) each mentor possesses distinct teaching capabilities due to varying capacity gaps, and (3) the performance gap evolves during training, we propose ClassroomKD (Figure 1(d)), a novel multi-mentor distillation framework inspired by classroom dynamics (see Appendix E). Our method introduces two key modules (Figure 1(e)) designed to address the following questions:

Q1: Which mentors are effective teachers for a given data sample?

We introduce the Knowledge Filtering Module to intelligently select mentors. This module dynamically ranks all mentors based on their performance for each input, activating only those with sufficient performance. A mentor is deemed effective and activated if its predictions are accurate and more confident than the student’s. This minimizes error accumulation and information loss.

Q2: How much information should the student learn from each active mentor?

Our Mentoring Module addresses this by tuning the teaching strategy based on the performance gap between the student and each active mentor. Specifically, we adjust each mentor’s distillation temperature to control the teaching pace, allowing the student to appropriately weigh information received from each mentor before integrating it into its own knowledge.

By addressing these questions iteratively, ClassroomKD ensures a continuously optimized learning process that adapts to the student’s evolving capabilities. Our contributions are as follows:

- 1.
ClassroomKD Framework: We introduce ClassroomKD, a novel multi-mentor distillation framework to dynamically select effective mentors and adapt teaching strategies.
- 2.
Knowledge Filtering Module: We develop a Knowledge Filtering Module to enhance distillation quality by selectively activating high-performance mentors, thereby reducing error accumulation and preventing information loss.
- 3.
Mentoring Module: We create a Mentoring Module that dynamically adjusts teaching strategies based on the performance gap between the student and each active mentor, optimizing the knowledge transfer process.
- 4.
Empirical Validation: Through extensive experiments on image classification (CIFAR-100 and ImageNet) and 2D human pose estimation (COCO Keypoints and MPII Human Pose), we demonstrate that ClassroomKD significantly outperforms state-of-the-art KD methods.

## 2 Related Work

### 2.1 Knowledge Distillation Approaches

Knowledge distillation (KD) (Hinton et al., ) is a widely adopted technique for compressing deep neural networks, where a smaller student model learns from a larger teacher model by minimizing the distance between their output probability distributions, or soft labels. Traditional KD methods primarily focus on logit-based distillation, where the student learns directly from the teacher’s output logits. Notable methods include PKT (Passalis & Tefas, ), which employs probabilistic knowledge transfer, FT (Kim et al., ), which transfers factorized feature representations, and AB (Heo et al., ), which leverages activation boundaries formed by hidden neurons.

Feature-based distillation methods transfer knowledge by aligning intermediate representations between the teacher and student. FitNets (Adriana et al., ) introduced this approach using intermediate feature maps for training. Later methods like AT (Zagoruyko & Komodakis, ), VID (Ahn et al., ), and CRD (Tian et al., ) enhance knowledge transfer by matching attention maps, utilizing variational information distillation, and employing contrastive learning, respectively.

Relation-based methods focus on preserving the structural relationships within the teacher’s feature maps. RKD (Park et al., ) maintains data point structures through relational knowledge distillation, while SP (Tung & Mori, ) and SRRL (Yang et al., ) optimize for similarity-preserving objectives. DIST (Huang et al., ) addresses large capacity gaps by applying a correlation-based loss to maintain both inter-class and intra-class relationships, enhancing distillation efficiency.

Recent approaches have explored more specialized distillation techniques. WSLD (Zhou et al., ) introduces weighted soft labels to balance bias-variance trade-offs, while One-to-All Spatial Matching KD (Lin et al., ) focuses on spatial matching techniques. OFA (Hao et al., ) optimizes feature-based KD by projecting features onto the logit space, significantly improving performance for heterogeneous models. To enhance distillation effectiveness, several methods have incorporated adaptive strategies. CTKD (Li et al., ) dynamically adjusts the temperature during training to gradually increase learning difficulty, and DTKD (Wei & Bai, ) employs real-time temperature scaling to improve knowledge transfer efficiency.

### 2.2 Multi-Teacher Knowledge Distillation

Multi-teacher distillation methods aim to further enhance student performance by leveraging an ensemble of mentors (You et al., ).

Online knowledge distillation has been particularly successful in this context. Deep Mutual Learning (DML) (Zhang et al., ) introduces a framework where multiple peer models learn from each other simultaneously during training, fostering collaborative learning among smaller networks and outperforming traditional one-way (offline) distillation. Other online methods include ONE (Zhu et al., ), OKDDip (Chen et al., ), and FFM (Li et al., ), which often outperform offline methods. Online distillation has also been extended to pose estimation tasks (Li et al., ). SHAKE (Li & Jin, ) proposed using proxy teachers with shadow heads to use the benefits of online distillation in offline settings.

To address the capacity gap in multi-teacher setups, Teacher-Assistant KD (TAKD) (Mirzadeh et al., ) employs intermediate-sized teacher assistants (TAs) to bridge the gap between the largest teacher and the student. However, sequential distillation through TAs can result in an "error avalanche," where errors propagate at each step, reducing final performance. Adaptive Ensemble Knowledge Distillation (AEKD) (Du et al., ) mitigates this issue by using an adaptive dynamic weighting strategy to reduce error propagation in the gradient space. Densely Guided KD (DGKD) (Son et al., ) further improves upon these methods by guiding each TA with both larger TAs and the main teacher, enabling a more gradual and effective transfer of knowledge. Additionally, DGKD introduces a strategy of randomly dropping mentors during training to expose the student to diverse learning sources, enhancing overall learning robustness.

While existing multi-teacher methods offer various mechanisms for knowledge distillation, they still grapple with challenges such as managing the capacity gap, mitigating error accumulation, and adapting to dynamic mentor-student relationships.

Figure: Figure 2: The ClassroomKD framework. comprises a Knowledge Filtering (KF) Module and a Mentoring Module. The KF Module optimizes learning by selectively incorporating feedback from higher-ranked mentors, reducing noise transfer and preventing error accumulation. The Mentoring Module adjusts mentor influence based on their performance relative to the student.
Refer to caption: x2.png

## 3 Methodology

ClassroomKD is a novel multi-mentor distillation framework inspired by real-world classroom environments. It is designed to address the challenges of large capacity gaps, error accumulation, and lack of dynamic adaptation. Our framework is illustrated in Figure 2.

Classroom Definition. A classroom comprises (1) a high-capacity teacher model, $t$, (2) a small student model, $s$, and (3) $n$ peer models of intermediate capacities, $\mathbb{P}=\{p_{i}\}_{i=1}^{n}$. We define $\mathbb{M}=\{t\}\cup\mathbb{P}$ as the set of pre-trained mentors that remain frozen during the student’s training process. At each training step, the student distills knowledge from a dynamically selected subset of mentors, called the active mentors ($\mathbb{M}^{\prime}\subseteq\mathbb{M}$). The set of all classroom models is denoted $\mathbb{C}=\{s\}\cup\mathbb{M}$. We use the Knowledge Filtering (KF) Module for intelligent mentor selection and the Mentoring Module to adjust the teaching pace based on the capacity gap of each mentor-student pair.

### 3.1 Knowledge Filtering Module

The KF Module is designed to intelligently select which mentors should contribute to the student’s learning process for each data sample. This selective approach mitigates error accumulation and prevents the student from learning from less effective mentors.

Let $\bm{x}=\{x_{k}\}_{k=1}^{N}$ be a batch of training data with size $N$, and $\bm{y}=\{y_{k}\}_{k=1}^{N}$ be the ground-truth labels. The batch inputs $\bm{x}$ are forwarded through all classroom models to obtain the predicted logits $\hat{\bm{y}}^{m}$, which are then converted to probabilities with a softmax operation. We isolate the probability assigned to the true class $\bm{y}$ and compute a weighted average of the correct prediction probability across the batch for each model. For all $m\in\mathbb{C}$, this is defined as:

$$ $\displaystyle\hat{\bm{y}}^{m}$ $\displaystyle=m(\bm{x})$ (1) $\displaystyle\bm{p}^{m}$ $\displaystyle=\mathrm{softmax}(\hat{\bm{y}}^{m})$ (2) $\displaystyle\bm{p}^{m}_{\text{gt}}$ $\displaystyle=\bm{p}^{m}[\bm{y}]$ (3) $\displaystyle w^{m}$ $\displaystyle=\frac{1}{N}\sum_{k=1}^{N}\bm{p}^{m}_{\text{gt}}(x_{k})$ (4) $$

The weights $w^{m}$ reflect the performance of model $m$ on the current training batch. We use the computed weights as a proxy for mentor suitability in the distillation process and rank mentors based on their relative performance to all classroom models:

$$ $\displaystyle r^{m}$ $\displaystyle=\lambda\left(\frac{w^{m}}{\sum_{m\in\mathbb{C}}{w^{m}}}\right)$ (5) $$

where $r^{m}$ is a normalized ranking score of model $m$, and $\lambda$ is a scaling parameter set to the number of mentors in the classroom. Active mentors $\mathbb{M}^{\prime}$ are defined as those with higher ranks than the student:

$$ $\displaystyle\mathbb{M}^{\prime}$ $\displaystyle=\{m\mid m\in\mathbb{M}\text{ and }r^{m}>r^{s}\}$ (6) $$

This ensures the student learns from high-quality sources by selecting mentors based on their performance ranks. This selective approach prevents error accumulation as only mentors outperforming the student can teach it, avoiding the propagation of errors from less effective mentors. Additionally, it avoids information loss by consistently selecting the best-performing mentors, unlike random mentor-dropping strategies (Son et al., ).

### 3.2 Mentoring Module

The Mentoring Module dynamically adjusts the influence of each active mentor based on the mentor-student performance gap. This adaptive teaching strategy facilitates effective knowledge transfer tailored to the student’s evolving ability to absorb information from each mentor.

The distillation loss minimizes KL divergence between the student and mentor’s output distributions:

$$ $\mathcal{L}_{distill}(P,Q;\tau)=\tau^{2}\cdot\text{KL}\left(\mathrm{softmax} \left({P}/{\tau}\right)\,\|\,\mathrm{softmax}\left({Q}/{\tau}\right)\right)$ (7) $$

where $P$ and $Q$ represent the logits from the mentor and student networks, respectively, and $\tau$ is a temperature hyperparameter that smooths the probability distributions during the distillation process.

The temperature $\tau$ controls the sharpness of the probability distributions, affecting the knowledge transfer from a mentor to the student. For each active mentor $m\in\mathbb{M}^{\prime}$, we adjust the distillation temperature $\tau^{m}$ based on the performance gap between the student and the mentor. The performance gap is measured as the difference in their ranking scores:

$$ $\displaystyle\Delta r^{m}$ $\displaystyle=|r^{m}-r^{s}|/r^{m}$ (8) $\displaystyle\tau^{m}$ $\displaystyle=1+\Delta r^{m}\cdot\tau$ (9) $$

Here, $\tau$ is the base temperature, and $\tau^{m}$ increases with $\Delta r^{m}$, which represents the mentor-student performance gap. A larger $\Delta r^{m}$ results in a higher $\tau^{m}$, smoothing the mentor’s output distribution. This adjustment theoretically slows down the distillation process by softening the mentor’s predictions, allowing the student to assimilate knowledge more gradually when the performance gap is large. Conversely, the student receives sharper, more direct guidance when the gap is small.

The total loss $\mathcal{L}$ is computed by combining a task-specific loss $\mathcal{L}_{\mathrm{task}}$ with the weighted distillation losses from all active mentors:

$$ $\mathcal{L}=\alpha\mathcal{L}_{\mathrm{task}}(\hat{\bm{y}}^{s},\bm{y})+\beta \sum_{m\in\mathbb{M}^{\prime}}\gamma^{m}\mathcal{L}_{\mathrm{distill}}(\hat{ \bm{y}}^{m},\hat{\bm{y}}^{s};\tau^{m})$ (10) $$

Here, $\alpha=r^{s}$ represents the student’s self-confidence, which scales the task-specific loss. As the student’s rank $r^{s}$ improves, $\alpha$ increases, encouraging the student to rely more on its own predictions. For each mentor $m$, $\gamma^{m}=r^{m}$ scales the corresponding distillation loss, where $r^{m}$ is the mentor’s rank relative to the student. $\beta$ is a hyperparameter to control the influence of distillation loss relative to the task loss. This weighing, along with the mentor-specific temperature $\tau^{m}$, ensures that higher-performing mentors have a greater influence on the student’s learning, with each mentor distilling knowledge at an appropriate rate based on the performance gap. We use Cross-Entropy Loss for classification and MSE Loss for pose estimation tasks.

This promotes independent learning by increasing the student’s reliance on its own task performance as its confidence grows. It also ensures that the student benefits from guidance based on the relative performance of the active mentors, effectively balancing task-specific training with distillation from the most suitable mentors. This dynamic and adaptive approach ensures optimized knowledge transfer, minimizes error accumulation, and enhances the overall performance of the student model.

## 4 Experiments

This section presents our experiments to evaluate the effectiveness of ClassroomKD using different datasets. We primarily use CIFAR-100 (Krizhevsky et al., ) classification for detailed comparisons with state-of-the-art single and multiple-teacher distillation methods. This also includes online approaches using multiple mentors. In addition, we also report results on ImageNet (Deng et al., ) classification and human pose estimation using the COCO Keypoints (Lin et al., ) and MPII Human Pose (Andriluka et al., ) datasets. Our results show that ClassroomKD outperforms existing methods under various settings, highlighting the robustness and adaptability of our method.

Implementation Details. For CIFAR-100, we train for 240 epochs with a batch size of 64, a learning rate of 0.05 decayed by 10% every 30 epochs, and a 120-epoch warm-up phase. We use SGD with 0.9 momentum and $5\times 10^{-4}$ weight decay. The temperature $\tau$ is set to 12 via grid search (Figure 3). For ImageNet, models are trained for 100 epochs with $\tau=8$. For pose datasets, models are trained for 210 epochs with $\tau=4$. The scaling factor $\lambda$ is $n+1$ for all experiments, where $n$ is the number of peers. We used $\beta=1.0$ for classification and $\beta=2.5$ for pose estimation. We follow standard training protocols, with mentors pre-trained and kept frozen.

Figure: Figure 3: Temperature selection. Grid search using fixed-temperature KD, with the best student performance at $\tau=12$, used as the base temperature in Eq. 9.

### 4.1 Results

CIFAR-100 Classification: Table 1 compares the performance of ClassroomKD to single-teacher distillation methods on the CIFAR-100 dataset. We evaluate various teacher-student pairings using both homogeneous and heterogeneous architectures. ClassroomKD, which is logit-based, performs better than logit-based methods in a majority of cases, as well as most feature-based and relation-based methods. In particular, when comparing with the recent state-of-the-art CTKD (Li et al., ) and DTKD (Wei & Bai, ) methods, both of which use adaptive temperatures, ClassroomKD is better than the former in all cases and the latter in 75% of the cases.

**Table 1: Comparison with single-teacher distillation methods on CIFAR-100 classification. We report top-1 accuracy (%). KD methods are grouped by feature, relation, and logit-based. Best values in logit-based methods are bold, second-best underlined, and overall best blue**
| Method | Homogeneous architectures | Heterogeneous architectures |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Teacher | R110 | R110 | R56 | VGG13 | VGG13 | R32×4 | W-40x2 | R50 | Swin-T |
| Student | R20 | R32 | R20 | VGG8 | MBV2 | SN-V2 | SN-V1 | MBV2 | R18 |
| NOKD | 69.06 | 71.14 | 69.06 | 70.68 | 64.60 | 71.82 | 70.50 | 64.60 | 74.01 |
| FitNets (Adriana et al., ) | 68.99 | 71.06 | 69.21 | 73.54 | 64.14 | 73.54 | 73.73 | 63.16 | 78.87 |
| AT (Zagoruyko & Komodakis, ) | 70.22 | 72.31 | 70.55 | 73.62 | 59.40 | 72.73 | 73.32 | - | - |
| VID (Ahn et al., ) | 70.16 | 72.61 | 70.38 | 73.96 | - | 73.40 | 73.61 | 67.57 | - |
| CRD (Tian et al., ) | 71.46 | 73.48 | 71.16 | 73.94 | 69.73 | 75.65 | 76.05 | 69.11 | 77.63 |
| SimKD (Chen et al., ) | - | - | - | 74.93 | - | 77.49 | - | - | - |
| SMKD (Lin et al., ) | 71.70 | 74.05 | 71.59 | 74.39 | - | - | - | - | - |
| RKD (Park et al., ) | 69.25 | 71.82 | 69.61 | 73.72 | 64.52 | 73.21 | 72.21 | 64.43 | 74.11 |
| SP (Tung & Mori, ) | 70.04 | 72.69 | 69.67 | 73.44 | 66.30 | 74.56 | 74.52 | - | - |
| SRRL (Yang et al., ) | 71.51 | 73.80 | - | 73.23 | 69.34 | 75.66 | 76.61 | - | - |
| DIST (Huang et al., ) | - | - | 71.75 | - | - | 77.35 | - | 68.66 | 77.75 |
| KD (Hinton et al., ) | 70.67 | 73.08 | 70.66 | 72.98 | 67.37 | 74.45 | 74.83 | 67.35 | 78.74 |
| PKT (Passalis & Tefas, ) | 70.25 | 72.61 | 70.34 | 73.37 | - | 74.69 | 73.89 | 66.52 | - |
| FT (Kim et al., ) | 70.22 | 72.37 | 69.84 | 73.42 | - | 72.50 | 72.03 | - | - |
| AB (Heo et al., ) | 69.53 | 70.98 | 69.47 | 74.27 | - | 74.31 | 73.34 | - | - |
| WSLD (Zhou et al., ) | 72.19 | 74.12 | 72.15 | - | - | 75.93 | 76.21 | - | - |
| CTKD (Li et al., ) | 70.99 | 73.52 | 71.19 | 73.52 | 68.46 | 75.31 | 75.78 | 68.47 | - |
| DTKD (Wei & Bai, ) | - | 74.07 | 72.05 | 74.12 | 69.01 | 76.19 | 76.29 | 69.10 | - |
| OFA (Hao et al., ) | - | - | - | - | - | - | - | - | 80.54 |
| Ours (2024) | 72.45 | 74.60 | 72.65 | 74.51 | 69.84 | 76.52 | 75.05 | 70.15 | 80.32 |

**Table 2: Comparison with multi-teacher distillation methods. Best and second-best values in offline methods are bold and underlined, respectively, and overall best in blue. *:trained on (coco+aic)**
| Method | Same Architectures | Mixed Architectures |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- |
| Teacher | WR40x2 | R110 | R56 | VGG13 | VGG13 | W-40x2 |
| Student | WR16x2 | R20 | R20 | VGG8 | MBV2 | SN-V1 |
| NOKD | 73.64 | 69.06 | 69.06 | 70.68 | 64.60 | 70.50 |
| DML (Zhang et al., ) | 74.83 | 70.55 | 70.24 | 72.86 | 66.30 | 74.52 |
| ONE (Zhu et al., ) | 74.68 | 70.77 | 70.43 | 72.01 | 66.26 | - |
| SHAKE (Li & Jin, ) | 75.78 | - | 71.62 | 73.85 | 68.81 | 76.42 |
| TAKD (Mirzadeh et al., ) | 75.04 | - | 70.77 | 73.67 | - | - |
| AEKD (Du et al., ) | 75.68 | 71.36 | 71.25 | 74.75 | 68.39 | 76.34 |
| EBKD (Kwon et al., ) |  |  |  | 74.10 | 68.24 | 76.61 |
| DGKD (Son et al., ) | 76.24 | - | 71.92 | 74.40 | - | - |
| CA-MKD (Zhang et al., ) | - | - | - | 74.30 | 69.41 | 77.94 |
| AVER | 74.98 | 71.20 | 71.08 | 73.18 | 62.94 | 73.00 |
| Ours | 76.51 | 72.45 | 72.65 | 74.51 | 69.84 | 75.18 |

CIFAR-100 Classification with Multiple Mentors. We compare our approach with online and offline strategies involving multiple mentors in Table 2(a). In addition to the teacher named in the table, our classroom uses five additional peers, as defined in Appendix A. ClassroomKD performs better than online methods like DML, ONE, and SHAKE, as well as the baseline offline method (AVER) in almost all cases. Our method also outperforms methods like TAKD, AEKD, and DGKD, which are specifically designed to address the capacity gap and error accumulation issues. In particular, the AEKD method achieves significantly poor results despite using four more mentors than us. We also have a comparable performance with the state-of-the-art CA-MKD method for networks they report.

ImageNet Classification with Multiple Mentors: In the ImageNet experiments, ClassroomKD demonstrates its scalability and effectiveness on a larger dataset. As shown in Table 2(c), ClassroomKD outperforms traditional KD and other multi-teacher methods, achieving higher top-1 and top-5 accuracy scores. This indicates that ClassroomKD maintains its superiority even as the complexity and size of the dataset increase, underscoring its robustness and adaptability.

Pose Estimation with Multiple Mentors. We also assess ClassroomKD’s performance on 2D human pose estimation tasks using the COCO Keypoints and MPII Human Pose datasets. Table 2(c) compares ClassroomKD against a simple multi-teacher baseline regarding keypoint detection accuracy and overall pose estimation performance. ClassroomKD achieves higher Average Precision (AP) scores, demonstrating its ability to effectively transfer structured knowledge from multiple mentors to the student model. This highlights ClassroomKD’s versatility and effectiveness beyond image classification tasks, extending its applicability to complex, structured prediction problems.

### 4.2 Ablation Studies

We conduct a series of ablation studies to understand the individual contributions of different components of our ClassroomKD framework, providing insights into our design choices.

**Table 3: Ablation study to assess the contribution of different components of ClassroomKD.**
| Student | Teacher | Peers | Adaptive Distillation | Top-1 Accuracy |
| --- | --- | --- | --- | --- |
| ✓ | ✗ | ✗ | ✗ | 63.31 |
| ✓ | ✓ | ✗ | ✗ | 63.35 |
| ✓ | ✓ | ✓ | ✗ | 65.96 |
| ✓ | ✓ | ✓ | ✓ | 68.52 |

Role of System Components. In Table 3(a), we observe a significant improvement when moving from single-teacher distillation (row 2) to a multi-mentor setup (row 3). The presence of multiple mentors, specifically the intermediate-sized peers, bridges the capacity gap between the large teacher and the small student. This gap is a well-known limitation in traditional KD, where the student struggles to fully comprehend the knowledge transferred from a much larger teacher. Introducing peers, which have capacities between the teacher and student, effectively provides a smoother learning gradient for the student, facilitating a more gradual and interpretable knowledge transfer.

The adaptive distillation strategy (row 4) boosts accuracy by 2.56%, highlighting the limitations of static distillation methods. By adjusting distillation based on the student’s progress and mentor outputs, ClassroomKD ensures more efficient learning, especially during critical phases where mentor usefulness varies. Table 3(b) shows that the KF Module improves accuracy from 65.96% to 68.49% by filtering out irrelevant knowledge, while the Mentoring Module dynamically adapts teaching strategies, raising performance to 67.25%. Together, these modules achieve the highest accuracy of 68.52%, ensuring both quality and adaptability in knowledge transfer.

We examine the classroom composition and further analyze our framework in the following sections.

#### 4.2.1 Classroom Size and Composition

This section examines the impact of both the number and diversity of mentors on student performance within ClassroomKD. Our experiments investigate different mentor configurations, including varying mentor quantities and diverse architectures and performance levels.

Figure: (a) Without controlling mentor architectures or performance levels, we train a student in classrooms with up to six mentors. The validation accuracy improves as the number of peers increases, but the marginal gain diminishes beyond five peers. We use these results to limit the size of our classrooms to six mentors in all subsequent experiments.
Refer to caption: extracted/5889827/figures/ablations/classroom/composition.png

Impact of peer quantity. Figure 4(a) and 4(c) illustrates the effect of increasing the number of peers in the classroom. Without any peers, the student achieves 63.35% top-1 accuracy. However, as peers are added, performance steadily improves, reaching 67.53% with five peers. This improvement demonstrates that incorporating intermediate mentors (peers) with varied capacities helps bridge the gap between the large teacher and small student, making knowledge transfer more effective. However, the performance improvement plateaus beyond five peers. This suggests that while adding mentors benefits learning, the gain diminishes beyond a certain point due to redundancy in the knowledge being transferred. Therefore, we limit our classrooms to six mentors in all subsequent experiments to balance efficiency and performance.

**Table 4: Effect of Mentor Diversity. We investigate the role of mentor diversity in terms of architecture and performance levels.**
| Classroom | Mentors | Params | Top-1 |
| --- | --- | --- | --- |
| Same | EN-B0 x6 | 24.8M | 67.53 |
| Diverse | VGG13, R8, R14, R20, SV1, SV2 | 12.3M | 68.52 |

Architectural Diversity (Table 4(a)): We observe that using mentors with diverse architectures (e.g., VGG, ResNet, and ShuffleNet) yields better performance (68.52%) compared to using multiple instances of the same architecture (67.53%). Interestingly, this improvement occurs despite the fact that the total parameter count of the diverse mentors (12.3M) is significantly lower than that of the homogeneous set (24.8M). This indicates that architectural diversity introduces richer and more varied learning signals, which are more effective for knowledge distillation.

Performance Diversity (Table 4(b)): We also evaluate the effect of mentor performance diversity by creating classrooms composed of mentors from different performance brackets. When mentors are homogeneous in terms of performance (either all low- or all high-performing), student performance remains lower. However, a diverse set of mentors, comprising both low- and high-performing peers, leads to the highest student accuracy (68.29%). This suggests that having varied knowledge sources across performance levels provides complementary learning experiences for the student, facilitating more robust distillation.

#### 4.2.2 Temperature in Mentoring Module

We explore the role of adaptive temperature (
$\tau$) in the Mentoring Module and its impact on bridging the capacity gap between classroom networks. Our approach adjusts the temperature dynamically based on the student’s learning progress, with higher $\tau$ values at the start to accommodate the larger capacity gap, which gradually decreases as the student’s understanding improves. This adaptive strategy allows mentors to effectively “slow down” the teaching process during early stages and accelerate it later, ensuring effective knowledge transfer.

Figure: Figure 5: Effect of temperature adaption. Our adaptive approach independently adjusts the temperature for each mentor (teacher and peers) over time, allowing them to optimize their teaching strategies dynamically across epochs.
Refer to caption: extracted/5889827/figures/ablations/temperature/adaption-new.png

In our experiments, using an adaptive $\tau$ strategy yields a significant improvement in student performance. The adaptive method, which adjusts $\tau$ based on the student’s progress, achieves a top-1 accuracy of 69.78%, compared to a static $\tau$ setup where performance remains lower (65.43% to 65.87% for fixed values). This demonstrates that adapting the teaching pace based on the student’s understanding leads to better learning outcomes.

Comparison with DTKD. We further evaluate our approach against DTKD’s dynamic temperature strategy by incorporating their method into our mentoring module. While DTKD shows minor improvements with $\tau=8$ for MobileNetV2, it falls short when using $\tau=12$ or for other student models such as ResNet20. This highlights DTKD’s dependence on the initial
$\tau$ value and its inability to fully address the capacity gap between the teacher and student. In contrast, our method adapts more effectively across different student and mentor configurations, achieving consistently better results by tailoring the teaching strategy dynamically throughout training.

#### 4.2.3 Ranking Strategies in KF Module

We study the effect of our ranking strategy in the KF Module, which dynamically activates the teacher and peers to guide the student. In Figure 6, we observe the evolution of ranks over time, where the teacher (red) consistently holds a higher rank than all other mentors because of its superior performance. Peer ranks (green) fluctuate, and ineffective peers are deactivated as their ranks fall below the student’s rank (blue) during training. This dynamic mentor activation prevents error accumulation from underperforming mentors and allows the student to progressively improve.

Figure: Figure 6: Rank-based mentor activation. Ranks evolve during training, reflecting the dynamic nature of capacity gaps. ClassroomKD uses high-quality mentors (red and green), deactivating ineffective mentors (black) who rank below the student (blue).
Refer to caption: extracted/5889827/figures/ablations/ranking/ranking.png

In Table 6, we explore an alternative ranking strategy (Method B) by replacing Eq. 5 with:

$$ $\displaystyle\bm{j}$ $\displaystyle=\text{argsort}({w^{m}\mid m\in\mathbb{C}})\quad\text{for }m\in \mathbb{C}$ (11) $\displaystyle r^{m}$ $\displaystyle=\lambda\cdot\bm{j}^{-1}(m)$ (12) $$

where $r^{m}$ is a ranking score, $\lambda$ is a scaling parameter set to 0.1, and $\bm{j}^{-1}(m)$ gives the index of model $m$ in a sorted list of weights. This results in uniformly distributed ranks (0.1, 0.2, 0.3, …) instead of the weighted rank distribution in our original formulation. The results show that the proposed ranking method works better. However, we note that even this alternative ranking computation performs better than baseline methods for multiple networks. This improvement stems from the rank-based weighting mechanism, which focuses the student’s learning on more challenging and discriminative classes, reducing sensitivity to noise and enhancing overall learning efficiency.

## 5 Conclusion

We presented ClassroomKD, a novel knowledge distillation framework that mimics a classroom environment, where a student learns from a diverse set of mentors. By selectively integrating feedback through the Knowledge Filtering (KF) Module and dynamically adjusting teaching strategies with the Mentoring Module, ClassroomKD ensures effective knowledge transfer and mitigates the issues of error accumulation and capacity gap. Our approach significantly improves the student’s performance in classification and pose estimation tasks, consistently outperforming traditional distillation methods. Limitations. While we demonstrated the efficacy of ClassroomKD on image classification and human pose estimation, its application to other domains and more complex tasks, such as object detection and segmentation, presents a promising avenue for future work. Despite the improvements, the framework introduces complexity, especially with respect to the mentor ranking and teaching adjustments, which can require careful tuning. Future work will explore further optimizations and expand the framework’s utility to broader tasks.

## Appendix A Training Protocols

Mentor Configuration. We use a predefined order for the mentor set in all experiments for consistency. Any deviations from this are clearly stated.

**Table 7: Mentor Configurations.We show the set of models used in all our experiments along with their respective top-1 accuracies and the ensemble performance. The size of the mentors, if all the peers were replaced by the teacher ($(n+1))t$), the size of the current mentors ($1tnp$), and student size are also mentioned. Model abbreviations: MB: MobileNet, SN: ShuffleNet, R: Resnet, W: WRN, EN: EfficientNet, SQ: SqueezeNet, RP: RTMPose, HR: HRNet, LHR: LiteHRNet, RG: RegNet**
|  | Mentors | Params (M) |  |  |  |  |  |  |  |
| --- | --- | --- | --- | --- | --- | --- | --- | --- | --- |
| $s$ | $t$ | $p_{1}$ | $p_{2}$ | $p_{3}$ | $p_{4}$ | $p_{5}$ | $(n+1)t$ | $1tnp$ | $s$ |
| CIFAR-100 Classification |  |  |  |  |  |  |  |  |  |
| R20 | R110 | R8 | R14 | SN-V2 | MBV2 | SN-V1 |  |  |  |
| (69.06) | (74.31) | (60.22) | (67.28) | (72.60) | (63.51) | (71.29) | 10.42 | 5.12 | 0.27 |
| R32 | R110 | R8 | R14 | SN-V2 | MBV2 | SN-V1 |  |  |  |
| (71.14) | (74.31) | (60.22) | (67.28) | (72.60) | (63.51) | (71.29) | 10.42 | 5.12 | 0.47 |
| R20 | R56 | R8 | R14 | SN-V2 | MBV2 | SN-V1 |  |  |  |
| (69.06) | (72.41) | (60.22) | (67.28) | (72.60) | (63.51) | (71.29) | 5.17 | 4.24 | 0.27 |
| VGG8 | VGG13 | R20 | MBV2 | SN-V2 | R56 | R110 |  |  |  |
| (70.36) | (74.64) | (69.06) | (63.51) | (72.60) | (72.41) | (74.31) | 56.77 | 14.50 | 3.96 |
| MBV2 | VGG13 | R8 | R14 | R20 | SN-V1 | SN-V2 |  |  |  |
| (63.51) | (74.64) | (60.22) | (67.28) | (69.06) | (71.29) | (72.60) | 56.77 | 12.31 | 0.81 |
| SN-V2 | R32x4 | R8 | R14 | R20 | MBV2 | SN-V1 |  |  |  |
| (72.60) | (79.42) | (60.22) | (67.28) | (69.06) | (63.51) | (71.29) | 44.62 | 9.739 | 1.35 |
| SN-V1 | W-40-2 | R20 | MBV2 | SN-V2 | R56 | VGG13 |  |  |  |
| (71.29) | (75.61) | (69.06) | (63.51) | (72.60) | (72.41) | (74.64) | 13.53 | 15.00 | 0.95 |
| MBV2 | R50 | R8 | R14 | R20 | SN-V1 | SN-V2 |  |  |  |
| (63.51) | (79.34) | (60.22) | (67.28) | (69.06) | (71.29) | (72.60) | 142.23 | 26.55 | 0.81 |
| SN-V1 | R32x4 | R8 | R14 | R20 | MBV2 | SN-V2 |  |  |  |
| (71.29) | (79.42) | (60.22) | (67.28) | (69.06) | (63.51) | (72.60) | 44.62 | 10.14 | 0.95 |
| W-16-2 | W-40-2 | R20 | MBV2 | SN-V2 | R56 | VGG13 |  |  |  |
| (73.64) | (75.61) | (69.06) | (63.51) | (72.60) | (72.41) | (74.64) | 13.53 | 14.98 | 0.70 |
| MBV2 | ENB0 | ENB0 | ENB0 | ENB0 | ENB0 | ENB0 |  |  |  |
| (63.51) | (73.21) | (60.23) | (61.03) | (63.60) | (66.87) | (72.70) | 24.81 | 24.81 | 0.81 |
| R18 | Swin-T(224) | SN-V2 | W-40-2 | VGG13 | R32x4 | - |  |  |  |
| (74.01) | (88.78) | (72.60) | (75.61) | (74.64) | (79.42) | - | 137.98 | 48.10 | 11.22 |
| ImageNet Classification |  |  |  |  |  |  |  |  |  |
| R34 | RG-Y320 | SQ1-1 | MBV2 | ENB3 | RG-Y016 | - |  |  |  |
| (73.31) | (80.74) | (58.18) | (71.88) | (78.54) | (77.67) | - | 725.23 | 173.22 | 21.79 |
| COCO Keypoints Estimation |  |  |  |  |  |  |  |  |  |
| RP-t | RP-l* | RP-s | RP-m | RP-l | - | - |  |  |  |
| (68.2) | (76.5) | (71.6) | (74.6) | (75.8) | - | - | - | - | - |
| MPII Human Pose Estimation |  |  |  |  |  |  |  |  |  |
| LHR-18 | HR-W32D | LHR-30 | HR-W32 | HR-W48 | - | - |  |  |  |
| (85.91) | (90.4) | (86.9) | (90.0) | (90.1) | - | - | - | - | - |
| LHR-18 | HR-W32D | SN-V2 | MBV2 | R50 | - | - |  |  |  |
| (85.91) | (90.4) | (82.8) | (85.4) | (88.2) | - | - | - | - | - |

Hardware and Software Configuration. We trained most of our CIFAR-100 experiments on a single V100-16GB GPU. The time required for an experiment ranged between 4 and 4.5 hours on average. We build our code on top of Image Classification SOTA repository(^1^11[https://github.com/hunto/image_classification_sota/](https://github.com/hunto/image_classification_sota/)) and MMPose, and use pretrained models from these libraries as our mentors.

## Appendix B Future Direction: ClassroomKD and Dataset Distillation

ClassroomKD shows strong potential in knowledge distillation, and one promising extension is its application in dataset distillation, which can further broaden its impact across various tasks.

Dataset distillation aims to create small, synthetic datasets that enable neural networks to achieve comparable performance to those trained on the original, much larger datasets. This approach reduces computational costs and storage requirements while maintaining model generalization. By optimizing a small set of representative training samples, a distilled dataset $S$ is generated such that a model trained on $S$ performs well on the original dataset $\mathcal{T}$. In our experiments, we use FRePo (Zhou et al., ) to create a distilled CIFAR-100 dataset, reducing each class to only 10 samples (Figure 7). Of these, 7 images per class are used for training, while the remaining 3 are used for testing.

Figure: Figure 7: Sample from the distilled CIFAR-100 dataset created using FRePo. The dataset is reduced to 10 representative images per class, where each image encapsulates key characteristics of the class. This distilled dataset significantly reduces storage and computational requirements while maintaining essential features for effective training.
Refer to caption: x3.png

As shown in Table 8, we conducted experiments on this distilled CIFAR-100 dataset and evaluated validation performance on the full CIFAR-100 dataset using the MobileNetV2 and ResNet-20 architectures. Notably, the standalone MobileNetV2 student achieves 31.00 on the distilled dataset, with 3.75% top-1 accuracy on the full validation set. However, applying ClassroomKD with 1 teacher and 5 peers significantly improves performance, reaching 44.34 on the distilled data and 6.30% top-1 accuracy on the full CIFAR-100 validation set. This is in stark contrast to the AVER approach, which results in only 2.33 on the distilled data and 1.51% top-1 accuracy on the full validation set using the same number of mentors. Similarly, ClassroomKD achieves superior results with ResNet-20, showing a notable 9.66 percentage point improvement on the distilled data compared to NOKD and a 1.85 percentage point gain on the full CIFAR-100 validation set.

**Table 8: Performance comparison on the distilled CIFAR-100 dataset and validation metrics on the full CIFAR-100 dataset. Results show top-1 accuracy on both the distilled dataset (7 images per class for training) and the full CIFAR-100 validation set. ClassroomKD (1 teacher, 5 peers) outperforms both the standalone student and AVER, demonstrating its efficacy in low-data regimes.**
| Student | MobileNetV2 | ResNet-20 |  |  |  |
| --- | --- | --- | --- | --- | --- |
| Method | Distilled Top-1 | Top-1 | Distilled Top-1 | Top-1 | Top-5 |
| NOKD | 31.00 | 3.75 | 50.00 | 3.08 | 12.50 |
| AVER | 2.33 | 1.51 | 32.00 | 3.55 | 15.24 |
| ClassroomKD | 44.34 | 6.30 | 59.66 | 4.93 | 17.81 |

These results suggest that ClassroomKD has strong potential to enhance performance on compact datasets, even where traditional methods fall short. By selectively leveraging the most effective mentors, ClassroomKD enables optimal knowledge transfer, making it a promising approach for dataset distillation. Additionally, combining ClassroomKD with dataset distillation can be extended to continual learning, where models from previous tasks act as mentors for new tasks. This approach could improve efficiency and performance in larger-scale tasks and real-world scenarios.

## Appendix C Analysis and Additional Results

### C.1 Per-Class Performance Improvement

We further analyze ClassroomKD’s effectiveness by examining the per-class performance improvements of the distilled student model compared to the baseline model (without knowledge distillation). To this end, we compare the class-level accuracy differences between ClassroomKD and a standard multi-teacher knowledge distillation (AVER) approach, both using the distilled CIFAR-100 dataset.

In Figure 8, we illustrate the performance differences between the ClassroomKD student and the baseline model on the left. ClassroomKD improves performance in 86 out of 100 classes while minimizing performance degradation in the remaining classes. In contrast, AVER (right) has a significantly smaller improvement, and the absolute performance degradation is more severe than with ClassroomKD. This demonstrates the benefit of our mentor ranking strategy, which dynamically selects mentors based on their relative performance and reduces the likelihood of detrimental knowledge transfer or error accumulation from multiple mentors.

Figure: Figure 8: Comparison of per-class performance gain over the NOKD baseline. With ClassroomKD (left), the distilled model improves performance on 86 classes. With multi-teacher KD without mentor ranking (right), significantly fewer classes improve, the absolute improvement is smaller, and the remaining classes experience larger performance degradation (red bars). This highlights the impact of our dynamic strategies in improving performance across different classes.
Refer to caption: extracted/5889827/figures/additional/ours-nokd.png

### C.2 Softmax for Ranking Mentor

Figure: Figure 9: Softmax for Ranking Mentors. We plot the softmax probabilities (y-axis) against the class indices (x-axis) of all models in a 1t5p classroom used. On the left, we show the distribution of confidence in model predictions. We zoom in on the target class on the right. The mentors with less softmax probability (or confidence) than the student (blue line) are deactivated to reduce error accumulation observed in traditional multi-teacher distillation methods.
[Uncaptioned image]: x4.png

### C.3 Pose Estimation Qualitative Results

Figure: (a) NOKD
Refer to caption: x6.png

## Appendix D ClassroomKD Algorithm

Figure: Algorithm 1 ClassroomKD

## Appendix E Classroom Learning Styles Survey

We conducted an online survey about learning styles and academic success in the classroom environment, in which forty (40) respondents participated. Most respondents (92.5%) were 18-45 years old, with 32.5% self-identifying as students, 22.5% as teachers or mentors, and 37.5% identifying as both. This survey aimed to gather insights into the various methods and strategies students and teachers employ to excel in their academic goals. In this appendix, we provide some statistics from the responses we received. These inspired the ClassroomKD approach introduced in the paper. Participation in the survey was voluntary, and participants could withdraw at any time without penalty.

Consent form for the survey

[Uncaptioned image]: extracted/5889827/figures/survey/consent.png

### E.1 Role of a Competitive Classroom Environment

In the first series of questions, we try to find out if students feel like they learn better in collaborative environments, which provide opportunities for healthy competition. The results showed positive response to collaboration among peers along with the teacher. However, competition was mostly detrimental to learning towards the end of the training period (after the completion of coursework and during their exams).

How does competition among peers affect your learning abilities?

[Uncaptioned image]: extracted/5889827/figures/survey/question_1.png

The survey further explored specific scenarios where competition was beneficial or detrimental

Competition among peers helps me when:

[Uncaptioned image]: extracted/5889827/figures/survey/question_2.png

Competition was found to be helpful during the learning phase (lecture period) of a semester. This competition can take the form of in-class discussions, group projects, or other collaborative activities. It encouraged active participation and knowledge sharing among students, fostering a collaborative learning atmosphere.

Competition among peers is distracting when:

[Uncaptioned image]: extracted/5889827/figures/survey/question_3.png

On the other hand, competition was often seen as distracting during critical phases like final exams or major project submissions. In these scenarios, the pressure to outperform peers led to decreased focus and increased anxiety, negatively impacting overall performance.

The insights from these responses were instrumental in designing the ClassroomKD framework. Recognizing the dual nature of competition, we incorporated mechanisms to balance collaborative learning with individual performance enhancement:

- •
Collaborative Learning Environment: By integrating multiple peers in the knowledge distillation process, ClassroomKD emulates a collaborative classroom where the student model benefits from diverse feedback. This mirrors the beneficial aspects of peer competition, fostering a supportive learning environment.
- •
Performance-Based Filtering: To mitigate the negative effects of competition, the Knowledge Filtering Module ensures that the student model learns from higher-ranked mentors only. This selective approach reduces the pressure from underperforming models and prevents the error propagation that could arise from unhealthy competition.

### E.2 Seeking Guidance

The second set of questions focused on understanding how students seek guidance when faced with challenges and the effectiveness of the feedback received. In these questions, we attempt to understand what prompts students to seek guidance from their mentors and how they handle it. The goal was to understand the correlation between when or whom students are asking for help and their success in achieving their objectives.

When your confidence drops, whom do you usually ask your doubts?

[Uncaptioned image]: extracted/5889827/figures/survey/question_4.png

The responses indicated a preference for different sources based on the perceived expertise and approachability. Most respondents consulted their peers or older siblings or tried to figure things out themselves. Peers were considered more approachable and could provide relatable explanations.

When you asked your questions to your teacher, what was their response?

[Uncaptioned image]: extracted/5889827/figures/survey/question_5.png

When asked about the nature of the teacher’s response, many participants noted that teachers often provided detailed explanations and additional resources. This thorough approach helped clarify doubts and improve understanding.

Did the teacher’s strategy help you gain confidence?

Many respondents confirmed that their confidence increased after receiving teacher feedback. This highlights the importance of effective mentoring in the learning process.

[Uncaptioned image]: extracted/5889827/figures/survey/question_6.png

These insights were crucial in shaping the Mentoring Module of ClassroomKD:

- •
Adaptive Mentoring: Inspired by the positive impact of teacher feedback, the Mentoring Module dynamically adjusts the teaching strategies based on the student’s current performance level. This ensures that the student model receives guidance tailored to its needs, similar to how a teacher would adjust their approach based on a student’s understanding.
- •
Selective Feedback: To emulate the preference for high-performing peers, the Knowledge Filtering Module ensures that the student model seeks feedback from higher-ranked peers and teachers. This selective process enhances the quality of knowledge transfer and boosts the student model’s confidence over time.

### E.3 Self-Assessment and Feedback

The final set of questions aimed to understand how students assess their own performance and the role of feedback in enhancing their learning experience.

How do you assess your performance on a test?

[Uncaptioned image]: extracted/5889827/figures/survey/question_7.png

Most of the responses suggest that students assess their performance based on peer comparison.

My confidence increases when I am appreciated:

[Uncaptioned image]: extracted/5889827/figures/survey/question_8.png

Respondents indicated that appreciation from others significantly boosted their confidence. Positive reinforcement motivated them to continue their efforts and strive for better results.

The responses highlighted the importance of self-assessment and constructive feedback, which influenced the design of ClassroomKD:

- •
Progressive Confidence Boosting: Reflecting the impact of appreciation on confidence, ClassroomKD incorporates a Progressive Confidence Boosting strategy. As the student model’s performance improves, its self-confidence (represented by the weighting parameter $\alpha$) increases. This dynamic adjustment ensures that the model’s learning is reinforced by its achievements, similar to how students gain confidence from positive feedback.
- •
Continuous Improvement: By integrating detailed feedback mechanisms through the Mentoring Module, ClassroomKD ensures that the student model continuously learns from its mistakes. The adaptive teaching strategies help the student model bridge the performance gap with mentors over time, fostering a continuous improvement cycle.

The survey responses provided valuable insights into effective learning strategies in a classroom environment. These insights were directly translated into the design and implementation of the ClassroomKD framework, ensuring that our knowledge distillation approach mirrors successful educational practices and optimizes student model performance.

## Peer Reviews

confidence: 4
contribution: 2
initial_rating: 3
presentation: 2
questions: Please see the above weaknesses.
soundness: 2
strengths: + The motivation that let more models teach the student model is not novel, but their approach to dynamically rank and select the better teachers per sample seems interesting and novel to me.

+ The paper is clearly written and easy to follow. The figure of motivation clearly shows the differences between their approach with earlier works. 

+ Experiments on CIFAR100 are very comprehensive.

+ Ablation studies are complete and cover many aspects of the proposed ClassroomKD.
summary: after reading the comments from other reviewers and corresponding responses, I would like to keep my initial score due to limited novelty and insufficient experiments.

———— 
This paper follows a setting in knowledge distillation where there are multiple teacher models involved. Instead of using traditional approach that all teacher models influence the student model, it proposes to first rank the performance and then filter out teacher models that underperform the student one. Afterwards, use a KL divergence driven weight assignment according to the performance gap. 

Experiments are conducted on CIFAR100, ImageNet and COCO. However, one thing to notice that on ImageNet and COCO pose estimation tasks, the authors only compare their method with the baseline student method without KD (NOKD).
weaknesses: I have the following concerns and hope to see the response from the authors.

1. How does the ClassroomKD compare to other multiple teachers approaches? For example, [A] proposes a dynamic framework that also learns from multiple teachers and multi-level knowledge. The proposed ranking the best seems very similar to the proposal in [A].

2. While experiments on CIFAR-100 are very comprehensive, the evaluation on ImageNet and COCO pose estimation are not sufficient. Could you clarify the meaning of the AVER performance in the two tables? Additionally, please explain the underlying method used for these experiments.

3. On CIFAR100, the performance gap between the proposed method and others is somewhat marginal. It may be not a good way to evaluate your method on CIFAR100 since it is considered overfitted in current research. Results on larger, more challenging datasets would provide more valuable insights into the effectiveness of your approach.
 
4. It would be beneficial to include comparisons with at least one state-of-the-art multi-teacher method on larger datasets such as ImageNet or COCO.


[A] Liu et al. Adaptive multi-teacher multi-level knowledge distillation. Neuralcomputing

---

confidence: 3
contribution: 3
initial_rating: 6
presentation: 4
questions: see the weaknesses
soundness: 4
strengths: 1. The framework's dynamic mentor selection and adaptive teaching strategies are highly innovative. It simulates a classroom environment, a novel approach compared to traditional methods, and effectively solves problems in multi-mentor distillation.
2. Theories behind the KF and Mentoring Modules are reasonable. The loss function construction is also sound. Experiments on diverse datasets with detailed settings and in-depth result analysis provide strong evidence for the method's effectiveness. It contributes to knowledge distillation research, inspiring future work. Its good performance in computer vision tasks offers practical solutions.
4. The paper has a clear structure and logical flow. The writing is clear, and figures aid understanding. The appendix enriches the paper.
summary: The paper presents ClassroomKD, a novel multi-mentor knowledge distillation framework inspired by classroom dynamics. It addresses challenges in multi-mentor distillation and consists of a Knowledge Filtering Module and a Mentoring Module. Experiments on multiple datasets show its superiority over existing methods.
weaknesses: 1. In-depth Analysis of Limitations: The limitations section could be enhanced. For example, more details on challenges in applying to other domains and the impact of framework complexity on performance and tuning difficulties are needed.
2. Exploration of More Practical Application Scenarios: While successful in computer vision, its potential in other areas like NLP and recommendation systems should be explored to show broader applicability.
3. Sensitivity Analysis of Hyperparameter Settings: A sensitivity analysis of hyperparameters would help researchers better understand and apply the method.
Overall, the paper has many strengths but could be improved in the mentioned areas.

---

confidence: 5
contribution: 2
initial_rating: 3
presentation: 3
questions: Please check the weaknesses above.
soundness: 2
strengths: Clear writing up and presentation.

Good coverage of the literature.

Reasonably good experiment design and setup and presentation.
summary: This work deals with a multi-teacher knowledge distillation method, where the key idea is to assess the fitness of each teacher at individual training sample level, and use that fitness to select the teachers for knowledge distillation in a weighted manner. The proposed method has been evaluated on a number of image classification tasks in comparison to previous methods.
weaknesses: **Novelty**:
1) This data sample conditioned teacher weighting idea has been proposed in [Ref-A] where even more advanced meta-learning based optimization algorithm was proposed for model training - bilevel optimization algorithm, in addition to other complexity and challenges, like data access to other domains and label scarcity. In general, this proposed work is a subset of Ref-A in techniques.
- [Ref-A] Wang Z, Ye M, Zhu X, Peng L, Tian L, Zhu Y. Metateacher: Coordinating multi-model domain adaptation for medical image classification. Advances in Neural Information Processing Systems. 2022 Dec 6

2) More justification should be added on why only those mentors/teachers with higher probability estimation on the true class label are selected and activated for distillation, whilst the remaining teachers are not. For example, in cases that the student model makes too confident precision for a specific training sample, those less confident teachers may provide a signal to soften this predictive confidence. This is because, in knowledge distillation, the key knowledge with the teacher is mostly about the class distribution, rather than seeking the maximum of true class probability. Under this consideration, the proposed knowledge filtering design is questionable. 

3) Similarly, more discussion and explanation about how Eq (8) represents the performance gap should be made. The similar concern holds here as the above.

**Experiments**:
1) The performance gap in comparison to previous art knowledge distillation is some limited, mostly within 0.5%. This suggests the benefits of this method is not significant. 

**Presentation issues**:
1) In general, the first appearance of previous work should come with a reference, e.g., no reference for DGKD (Line 50), and no reference for those mentioned methods in Fig 1's caption. The authors need to take a careful global check for this. 

2) The two concepts, performance gap (Line 52) and capacity gap (Ling 40), seem mixed and they are not properly defined, discussed and compared in the Introduction. 

**Overall**:
Given the limited novelty in terms of techniques, lacking of solid design rationales,  and not significant experimental advantage, I do not find enough significance of accepting this work at the current shape. More research works are needed to further enhance it for future submission.

---

confidence: 4
contribution: 3
initial_rating: 8
presentation: 4
questions: Please refer to the Strengths and Weaknesses.
soundness: 4
strengths: 1. This paper provides an in-depth analysis of existing methods and highlights their respective limitations.
2. The proposed framework is effective, and some experimental results look good.
3. The paper is well-written and easy to understand.
summary: This paper presents a knowledge distillation framework ClassroomKD to improve knowledge transfer from multiple mentors to a student model by dynamically selecting mentors based on their effectiveness for each data sample. The framework includes a Knowledge Filtering Module, which ranks and activates high-quality mentors, and a Mentoring Module, which adjusts each mentor's influence according to the performance gap with the student. Experiments on CIFAR-100, ImageNet, COCO Keypoints, and MPII Human Pose indicate that ClassroomKD performs competitively with existing methods, suggesting that adaptive mentor selection can enhance knowledge transfer and model performance.
weaknesses: 1. In the second section, recent literatures on knowledge distillation from the past two years is limited, and it is recommended to include additional references. For example: [1] Logit standardization in knowledge distillation  [2] Class attention transfer based knowledge distillation.
2. In Figure 2, based on the article's content, the indicator label for Active mentors should be "M’"
3. According to the results in Table 1, the improvement achieved by the proposed method compared with the single-teacher distillation methods appears modest. Theoretically, the use of multiple mentors should yield a more significant improvement than a single mentor. It is recommended to conduct further experimental analysis to explore this aspect.

Meta-review: This paper introduces a multi-mentor knowledge distillation framework known as ClassroomKD. It includes a Knowledge Filtering Module that ranks and activates high-quality mentors and a Mentoring Module that adjusts each mentor's influence based on the performance gap with the student. Experiments conducted on CIFAR-100, ImageNet, COCO Keypoints, and MPII Human Pose demonstrate that ClassroomKD performs competitively compared to existing methods.

The paper received mixed reviews, with scores of 6, 5, 3, and 3, leading to an average score of 4.25. Despite a rebuttal, concerns regarding its limited technical novelty and insufficient experimental validation remain unaddressed. Therefore, the Area Chair recommends rejection at this time.

Decision: Reject