Teacher-student Paradigm Research Articles

How a student becomes a teacher: learning and forgetting through spectral methods

In theoretical machine learning, the teacher–student paradigm is often employed as an effective metaphor for real-life tuition. A student network is trained on data generated by a fixed teacher network until it matches the instructor’s ability to cope with the assigned task. The above scheme proves particularly relevant when the student network is overparameterized (namely, when larger layer sizes are employed) as compared to the underlying teacher network. Under these operating conditions, it is tempting to speculate that the student ability to handle the given task could be eventually stored in a sub-portion of the whole network. This latter should be to some extent reminiscent of the frozen teacher structure, according to suitable metrics, while being approximately invariant across different architectures of the student candidate network. Unfortunately, state-of-the-art conventional learning techniques could not help in identifying the existence of such an invariant subnetwork, due to the inherent degree of non-convexity that characterizes the examined problem. In this work, we take a decisive leap forward by proposing a radically different optimization scheme which builds on a spectral representation of the linear transfer of information between layers. The gradient is hence calculated with respect to both eigenvalues and eigenvectors with negligible increase in terms of computational and complexity load, as compared to standard training algorithms. Working in this framework, we could isolate a stable student substructure, that mirrors the true complexity of the teacher in terms of computing neurons, path distribution and topological attributes. When pruning unimportant nodes of the trained student, as follows a ranking that reflects the optimized eigenvalues, no degradation in the recorded performance is seen above a threshold that corresponds to the effective teacher size. The observed behavior can be pictured as a genuine second-order phase transition that bears universality traits. Code is available at: https://github.com/Jamba15/Spectral-regularization-teacher-student/tree/master.

Sparsely-Supervised Object Tracking.

Recent years have witnessed the incredible performance boost of data-driven deep visual object trackers. Despite the success, these trackers require millions of sequential manual labels on videos for supervised training, implying the heavy burden of human annotating. This raises a crucial question: how to train a powerful tracker from abundant videos using limited manual annotations? In this paper, we challenge the conventional belief that frame-by-frame labeling is indispensable, and show that providing a small number of annotated bounding boxes in each video is sufficient for training a strong tracker. To facilitate that, we design a novel SParsely-supervised Object Tracking (SPOT) framework. It regards the sparsely annotated boxes as anchors and progressively explores in the temporal span to discover unlabeled target snapshots. Under the teacher-student paradigm, SPOT leverages the unique transitive consistency inherent in the tracking task as supervision, extracting knowledge from both anchor snapshots and unlabeled target snapshots. We also utilize several effective training strategies, i.e., IoU filtering, asymmetric augmentation, and temporal calibration to further improve the training robustness of SPOT. The experimental results demonstrate that, given less than 5 labels for each video, trackers trained via SPOT perform on par with their fully-supervised counterparts. Moreover, our SPOT exhibits two desirable properties: 1) SPOT enables us to fully exploit large-scale video datasets by efficiently allocating sparse labels to more videos even under a limited labeling budget; 2) when equipped with a target discovery module, SPOT can even learn from purely unlabeled videos for performance gain. We hope this work could inspire the community to rethink the current annotation principles and make a step towards practical label-efficient deep tracking.

ADPS: Asymmetric Distillation Postsegmentation for Image Anomaly Detection.

Knowledge distillation-based anomaly detection (KDAD) methods rely on the teacher-student paradigm to detect and segment anomalous regions by contrasting the unique features extracted by both networks. However, existing KDAD methods suffer from two main limitations: 1) the student network can effortlessly replicate the teacher network's representations and 2) the features of the teacher network serve solely as a "reference standard" and are not fully leveraged. Toward this end, we depart from the established paradigm and instead propose an innovative approach called asymmetric distillation postsegmentation (ADPS). Our ADPS employs an asymmetric distillation paradigm that takes distinct forms of the same image as the input of the teacher-student networks, driving the student network to learn discriminating representations for anomalous regions. Meanwhile, a customized Weight Mask Block (WMB) is proposed to generate a coarse anomaly localization mask that transfers the distilled knowledge acquired from the asymmetric paradigm to the teacher network. Equipped with WMB, the proposed postsegmentation module (PSM) can effectively detect and segment abnormal regions with fine structures and clear boundaries. Experimental results demonstrate that the proposed ADPS outperforms the state-of-the-art methods in detecting and segmenting anomalies. Surprisingly, ADPS significantly improves average precision (AP) metric by 9% and 20% on the MVTec anomaly detection (AD) and KolektorSDD2 datasets, respectively.

LENAS: Learning-Based Neural Architecture Search and Ensemble for 3-D Radiotherapy Dose Prediction.

Radiation therapy treatment planning requires balancing the delivery of the target dose while sparing normal tissues, making it a complex process. To streamline the planning process and enhance its quality, there is a growing demand for knowledge-based planning (KBP). Ensemble learning has shown impressive power in various deep learning tasks, and it has great potential to improve the performance of KBP. However, the effectiveness of ensemble learning heavily depends on the diversity and individual accuracy of the base learners. Moreover, the complexity of model ensembles is a major concern, as it requires maintaining multiple models during inference, leading to increased computational cost and storage overhead. In this study, we propose a novel learning-based ensemble approach named LENAS, which integrates neural architecture search with knowledge distillation for 3-D radiotherapy dose prediction. Our approach starts by exhaustively searching each block from an enormous architecture space to identify multiple architectures that exhibit promising performance and significant diversity. To mitigate the complexity introduced by the model ensemble, we adopt the teacher-student paradigm, leveraging the diverse outputs from multiple learned networks as supervisory signals to guide the training of the student network. Furthermore, to preserve high-level semantic information, we design a hybrid loss to optimize the student network, enabling it to recover the knowledge embedded within the teacher networks. The proposed method has been evaluated on two public datasets: 1) OpenKBP and 2) AIMIS. Extensive experimental results demonstrate the effectiveness of our method and its superior performance to the state-of-the-art methods. Code: github.com/hust-linyi/LENAS.

Calibrated Teacher for Sparsely Annotated Object Detection

Fully supervised object detection requires training images in which all instances are annotated. This is actually impractical due to the high labor and time costs and the unavoidable missing annotations. As a result, the incomplete annotation in each image could provide misleading supervision and harm the training. Recent works on sparsely annotated object detection alleviate this problem by generating pseudo labels for the missing annotations. Such a mechanism is sensitive to the threshold of the pseudo label score. However, the effective threshold is different in different training stages and among different object detectors. Therefore, the current methods with fixed thresholds have sub-optimal performance, and are difficult to be applied to other detectors. In order to resolve this obstacle, we propose a Calibrated Teacher, of which the confidence estimation of the prediction is well calibrated to match its real precision. In this way, different detectors in different training stages would share a similar distribution of the output confidence, so that multiple detectors could share the same fixed threshold and achieve better performance. Furthermore, we present a simple but effective Focal IoU Weight (FIoU) for the classification loss. FIoU aims at reducing the loss weight of false negative samples caused by the missing annotation, and thus works as the complement of the teacher-student paradigm. Extensive experiments show that our methods set new state-of-the-art under all different sparse settings in COCO. Code will be available at https://github.com/Whileherham/CalibratedTeacher.

Face Synthesis With a Focus on Facial Attributes Translation Using Attention Mechanisms

Synthesis of face images by translating facial attributes is an important problem in computer vision and biometrics and has a wide range of applications in forensics, entertainment, etc. Recent advances in deep generative networks have made progress in synthesizing face images with certain target facial attributes. However, visualizing and interpreting generative adversarial networks (GANs) is a relatively unexplored area and generative models are still being employed as black-box tools. This paper takes the first step to visually interpret conditional GANs for facial attribute translation by using a gradient-based attention mechanism. Next, a key innovation is to include new learning objectives for knowledge distillation using attention in generative adversarial training, which result in improved synthesized face results, reduced visual confusions and boosted training for GANs in a positive way. Firstly, visual attentions are calculated to provide interpretations for GANs. Secondly, gradient-based visual attentions are used as knowledge to be distilled in a teacher-student paradigm for face synthesis with focus on facial attributes translation tasks in order to improve the performance of the model. Finally, it is shown how “pseudo”-attentions knowledge distillation can be employed during the training of face synthesis networks when teacher and student networks are trained to generate different facial attributes. The approach is validated on facial attribute translation and human expression synthesis with both qualitative and quantitative results being presented.

Modifying the softening process for knowledge distillation

The prime focus of knowledge distillation (KD) seeks a light proxy termed student to mimic the outputs of its heavy neural networks termed teacher, and makes the student run real-time on the resource-limited devices. This paradigm requires aligning the soft logits of both teacher and student. However, few doubts whether the process of softening the logits truly give full play to the teacher-student paradigm. In this paper, we launch several analyses to delve into this issue from scratch. Subsequently, several simple yet effective functions are devised to replace the vanilla KD. The ultimate function can be an effective alternative to its original counterparts and work well with other skills like FitNets. To claim this point, we conduct several visual tasks on individual benchmarks, and experimental results verify the potential of our proposed function in terms of performance gains. For example, when the teacher and student networks are ShuffleNetV2-1.0 and ShuffleNetV2-0.5, our proposed method achieves 40.88%top-1 error rate on Tiny ImageNet.

Towards interpreting deep neural networks via layer behavior understanding

Deep neural networks (DNNs) have achieved success in many machine learning tasks. However, how to interpret DNNs is still an open problem. In particular, how do hidden layers behave is not clearly understood. In this paper, relying on a teacher-student paradigm, we seek to understand the layer behaviors of DNNs by “monitoring” the distribution evolution for both across-layer and single-layer along the depth and training epochs, respectively. Relying on the optimal transport theory, we employ the Wasserstein distance (W-distance) to measure the divergence between the layer distribution and the target distribution. Theoretically, we prove that (i) the W-distance between the distribution of any layer and the target distribution tends to decrease along the depth; (ii) for a specific layer, the W-distance between the distribution in an iteration and the target distribution tends to decrease along training epochs; (iii) a deeper layer, however, is not always better than a shallower layer. Relying on these properties, we are able to propose an early-exit inference method to improve the performance of the multi-label classification. Moreover, our results help to analyze the stability of layer distributions and explain why auxiliary losses are helpful in training DNNs. Extensive experiments justify our theoretical findings.

When Pansharpening Meets Graph Convolution Network and Knowledge Distillation

In this article, we propose a novel graph convolutional network (GCN) for pansharpening, defined as GCPNet, which consists of three main modules: the spatial GCN module (SGCN), the spectral band GCN module (BGCN), and the atrous spatial pyramid module (ASPM). Specifically, due to the nature of GCN, the proposed SGCN and BGCN are capable of exploring the long-range relationship between the object and the global state in the spatial and spectral aspects, which benefits pansharpened results and has not been fully investigated before. In addition, the designed ASPM is equipped with multiscale atrous convolutions and learns richer local feature information, so as to cover the objects of different sizes in satellite images. To further enhance the representation of our proposed GCPNet, asynchronous knowledge distillation is introduced to provide compact features by heterogeneous task imitation in a teacher–student paradigm. In the paradigm, the teacher network acts as a variational autoencoder to extract compact features of the ground-truth MS images. The student network, devised for pansharpening, is trained with the assistance of the teacher network to transfer the important information of the expected ground-truth MS images. Extensive experimental results on different satellite datasets demonstrate that our proposed network outperforms the state-of-the-art methods both visually and quantitatively. The source code is released at <uri xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">https://github.com/Keyu-Yan/GCPNet</uri> .

When students show some initiative: Two experiments on the benefits of greater agentic engagement

Correlational and longitudinal research suggests that agentically-engaged students experience multiple educational benefits. Recognizing this, two experiments tested the causal capacity of manipulated agentic engagement to create three categories of benefits: a supportive learning environment; motivational satisfactions; and effective functioning (e.g., engagement, performance). Study 1 used the teacher–student laboratory paradigm to place 121 same-sex pairs of preservice teachers into the roles of teacher and student during a videotaped 12-min instructional episode. Teachers were randomly assigned to be autonomy supportive or not, while students were randomly assigned to be agentically engaged or not. MANOVAs on 10 self-reported and rater-scored dependent measures showed that manipulated agentic engagement enhanced both a supportive learning environment and greater motivational satisfaction but not more effective functioning. Study 2 used the same teacher-student paradigm to randomly assign 74 same-sex pairs into one of three conditions: agentic engagement to change the environment (as in Study 1); agentic engagement to change one's functioning (a new Study 2 manipulation); and a neutral control. The first manipulation again enhanced the supportive learning environment and motivational satisfaction but not effective functioning, while the second manipulation produced no benefits. Collectively, these findings confirm some limited causal benefits from agentic engagement and therefore provide guidance for future research, including the design and implementation of student-focused agentic engagement interventions.

Learning Student Networks via Feature Embedding.

Deep convolutional neural networks have been widely used in numerous applications, but their demanding storage and computational resource requirements prevent their applications on mobile devices. Knowledge distillation aims to optimize a portable student network by taking the knowledge from a well-trained heavy teacher network. Traditional teacher-student-based methods used to rely on additional fully connected layers to bridge intermediate layers of teacher and student networks, which brings in a large number of auxiliary parameters. In contrast, this article aims to propagate information from teacher to student without introducing new variables that need to be optimized. We regard the teacher-student paradigm from a new perspective of feature embedding. By introducing the locality preserving loss, the student network is encouraged to generate the low-dimensional features that could inherit intrinsic properties of their corresponding high-dimensional features from the teacher network. The resulting portable network, thus, can naturally maintain the performance as that of the teacher network. Theoretical analysis is provided to justify the lower computation complexity of the proposed method. Experiments on benchmark data sets and well-trained networks suggest that the proposed algorithm is superior to state-of-the-art teacher-student learning methods in terms of computational and storage complexity.

Creating DALI, a Large Dataset of Synchronized Audio, Lyrics, and Notes

The DALI dataset is a large dataset of time-aligned symbolic vocal melody notations (notes) and lyrics at four levels of granularity. DALI contains 5358 songs in its first version and 7756 for the second one. In this article, we present the dataset, explain the developed tools to work the data and detail the approach used to build it. Our method is motivated by active learning and the teacher-student paradigm. We establish a loop whereby dataset creation and model learning interact, benefiting each other. We progressively improve our model using the collected data. At the same time, we correct and enhance the collected data every time we update the model. This process creates an improved DALI dataset after each iteration. Finally, we outline the errors still present in the dataset and propose solutions to global issues. We believe that DALI can encourage other researchers to explore the interaction between model learning and dataset creation, rather than regarding them as independent tasks.

Changing the Learning Environment: Teachers and Students’ Collaboration in Creating Digital Games

Aim/Purpose: The current study examines the impact of an intervention program to train teachers to collaborate with their students while creating digital games. Background: Teachers seem unable to leverage the potential of ICT to present students with a rich learning environment. ICT integration is usually at a relatively simple and concrete level without changing the traditional teacher-student paradigm. Methodology: The study is both quantitative and qualitative. Participants were 63 active teachers studying in the M.Ed. program at a teacher education college. The teachers responded to a series of pre- and post-questionnaires and wrote a concluding reflection. Contribution: Teaching based on creating digital games, combined with teacher-class collaboration, is a viable and real alternative of constructivist teaching, adapted to different learners. Findings: The SEM path analysis showed that it was only after the intervention that the lower the teachers’ resistance to changing teaching patterns, the higher their intrinsic motivation to learn an innovative pedagogical-technological program and likewise the sense of mastery of 21st-century skills, resulting in a positive attitude towards classroom collaboration. The qualitative findings reveal eight categories dealing with two main themes: the first is professional development, including conceptual, behavioral and emotional change, and the second is the teachers’ perception of the learners. Recommendations for Practitioners: Teacher training should be ongoing in order to change teaching-learning processes and promote an active approach based on constructive principles, 21st-century skills and collaboration between teachers and students in a computer environment. Recommendation for Researchers: Future studies should start by sampling teachers and education professionals who have convenient access to technology in their teaching-learning environment. Impact on Society: Collaboration between teachers and students in creating learning games in a computer environment and teacher-class collaboration, in general, require very different training than that which exists today. Hence there should be some rethinking of teacher training. The proposed pedagogical model is one such idea in the right direction. Future Research: A larger study with a greater number of participants, including a control group, should be conducted.

The Politics of Personal Pedagogy: Examining Teacher Identities

These short essays address the political nature of teaching dance in higher education from various perspectives. Issues of identity, authority, power, expectation, and assumption are addressed within the context of the teacher-student paradigm. From personal perspectives, each author examines the ways in which who they are affects what and how they teach, how they perceive their students, and how students perceive them. Four topics include: the politics of gender in dance pedagogy (Risner); the politics of a Christian educator in the academy (Clemente); the politics of race in the classroom (Hubbard); and, the politics of teacher and student identity in a post-feminist era (Kerr-Berry).

EFFECTS OF MITIGATING INFORMATION ON AROUSAL AND RETALIATORY AGGRESSION

An experiment based on teacher-student paradigm was conducted to examine effects of mitigating information on arousal and retaliatory aggression. The timing of presentation of mitigating information (post -vs. pre -attack) and the degree of mitigating information (medium, vs. high) ware manipulated as independent variables, and the group which didn't have mitigating information was also tested as a control group. The sbjects were 44 university students. They were strongly attacked before they retaliated.The main findings were as follows;1. Mitigating information given before an attack of another person (who was a colleague of the experimenter) suppressed the physiological arousal elicited by the attack, but if it was given after the attack, it could not suppress the arousal.2. Two types of retaliatory aggression (indirect aggression, direct aggression) were found.3. In indirect aggression, mitigating information suppressed retaliatory aggression only when it was given before attack regardless of the degree of the information. It was suggested that indirect aggression was influenced mainly by physiological variables.4. In direct aggression, retaliatory aggression was influenced by both the timing of presentation and degree of mitigating information. It was suggested that direct aggression was influenced by both physiological and cognitive variables.