Knowledge distillation is a standard teacher-student learning framework to train a light-weight student network under the guidance of a well-trained large teacher network. As an effective teaching strategy, interactive teaching has been widely employed at school to motivate students, in which teachers not only provide knowledge but also give constructive feedback to students upon their responses, to improve their learning performance. In this work, we propose an InterActive Knowledge Distillation (IAKD) scheme to leverage the interactive teaching strategy for efficient knowledge distillation. In the distillation process, the interaction between teacher and student networks is implemented by a swapping-in operation: randomly replacing the blocks in the student network with the corresponding blocks in the teacher network. In the way, we directly involve the teacher's powerful feature transformation ability to largely boost the student's performance. Experiments with typical settings of teacher-student networks demonstrate that the student networks trained by our IAKD achieve better performance than those trained by conventional knowledge distillation methods on diverse image classification datasets.

0
下载
关闭预览

相关内容

IFIP TC13 Conference on Human-Computer Interaction是人机交互领域的研究者和实践者展示其工作的重要平台。多年来,这些会议吸引了来自几个国家和文化的研究人员。官网链接:http://interact2019.org/

The advanced performance of depth estimation is achieved by the employment of large and complex neural networks. While the performance has still been continuously improved, we argue that the depth estimation has to be accurate and efficient. It's a preliminary requirement for real-world applications. However, fast depth estimation tends to lower the performance as the trade-off between the model's capacity and accuracy. In this paper, we attempt to archive highly accurate depth estimation with a light-weight network. To this end, we first introduce a compact network that can estimate a depth map in real-time. We then technically show two complementary and necessary strategies to improve the performance of the light-weight network. As the number of real-world scenes is infinite, the first is the employment of auxiliary data that increases the diversity of training data. The second is the use of knowledge distillation to further boost the performance. Through extensive and rigorous experiments, we show that our method outperforms previous light-weight methods in terms of inference accuracy, computational efficiency and generalization. We can achieve comparable performance compared to state-of-the-of-art methods with only 1% parameters, on the other hand, our method outperforms other light-weight methods by a significant margin.

0
0
下载
预览

Knowledge distillation (KD) has recently emerged as an efficacious scheme for learning compact deep neural networks (DNNs). Despite the promising results achieved, the rationale that interprets the behavior of KD has yet remained largely understudied. In this paper, we introduce a novel task-oriented attention model, termed as KDExplainer, to shed light on the working mechanism underlying the vanilla KD. At the heart of KDExplainer is a Hierarchical Mixture of Experts (HME), in which a multi-class classification is reformulated as a multi-task binary one. Through distilling knowledge from a free-form pre-trained DNN to KDExplainer, we observe that KD implicitly modulates the knowledge conflicts between different subtasks, and in reality has much more to offer than label smoothing. Based on such findings, we further introduce a portable tool, dubbed as virtual attention module (VAM), that can be seamlessly integrated with various DNNs to enhance their performance under KD. Experimental results demonstrate that with a negligible additional cost, student models equipped with VAM consistently outperform their non-VAM counterparts across different benchmarks. Furthermore, when combined with other KD methods, VAM remains competent in promoting results, even though it is only motivated by vanilla KD.

0
0
下载
预览

Semi-supervised learning on graphs is an important problem in the machine learning area. In recent years, state-of-the-art classification methods based on graph neural networks (GNNs) have shown their superiority over traditional ones such as label propagation. However, the sophisticated architectures of these neural models will lead to a complex prediction mechanism, which could not make full use of valuable prior knowledge lying in the data, e.g., structurally correlated nodes tend to have the same class. In this paper, we propose a framework based on knowledge distillation to address the above issues. Our framework extracts the knowledge of an arbitrary learned GNN model (teacher model), and injects it into a well-designed student model. The student model is built with two simple prediction mechanisms, i.e., label propagation and feature transformation, which naturally preserves structure-based and feature-based prior knowledge, respectively. In specific, we design the student model as a trainable combination of parameterized label propagation and feature transformation modules. As a result, the learned student can benefit from both prior knowledge and the knowledge in GNN teachers for more effective predictions. Moreover, the learned student model has a more interpretable prediction process than GNNs. We conduct experiments on five public benchmark datasets and employ seven GNN models including GCN, GAT, APPNP, SAGE, SGC, GCNII and GLP as the teacher models. Experimental results show that the learned student model can consistently outperform its corresponding teacher model by 1.4% - 4.7% on average. Code and data are available at https://github.com/BUPT-GAMMA/CPF

0
9
下载
预览

Knowledge graph (KG) plays an increasingly important role in recommender systems. A recent technical trend is to develop end-to-end models founded on graph neural networks (GNNs). However, existing GNN-based models are coarse-grained in relational modeling, failing to (1) identify user-item relation at a fine-grained level of intents, and (2) exploit relation dependencies to preserve the semantics of long-range connectivity. In this study, we explore intents behind a user-item interaction by using auxiliary item knowledge, and propose a new model, Knowledge Graph-based Intent Network (KGIN). Technically, we model each intent as an attentive combination of KG relations, encouraging the independence of different intents for better model capability and interpretability. Furthermore, we devise a new information aggregation scheme for GNN, which recursively integrates the relation sequences of long-range connectivity (i.e., relational paths). This scheme allows us to distill useful information about user intents and encode them into the representations of users and items. Experimental results on three benchmark datasets show that, KGIN achieves significant improvements over the state-of-the-art methods like KGAT, KGNN-LS, and CKAN. Further analyses show that KGIN offers interpretable explanations for predictions by identifying influential intents and relational paths. The implementations are available at https://github.com/huangtinglin/Knowledge_Graph_based_Intent_Network.

0
3
下载
预览

Knowledge distillation has demonstrated encouraging performances in deep model compression. Most existing approaches, however, require massive labeled data to accomplish the knowledge transfer, making the model compression a cumbersome and costly process. In this paper, we investigate the practical few-shot knowledge distillation scenario, where we assume only a few samples without human annotations are available for each category. To this end, we introduce a principled dual-stage distillation scheme tailored for few-shot data. In the first step, we graft the student blocks one by one onto the teacher, and learn the parameters of the grafted block intertwined with those of the other teacher blocks. In the second step, the trained student blocks are progressively connected and then together grafted onto the teacher network, allowing the learned student blocks to adapt themselves to each other and eventually replace the teacher network. Experiments demonstrate that our approach, with only a few unlabeled samples, achieves gratifying results on CIFAR10, CIFAR100, and ILSVRC-2012. On CIFAR10 and CIFAR100, our performances are even on par with those of knowledge distillation schemes that utilize the full datasets. The source code is available at https://github.com/zju-vipa/NetGraft.

0
4
下载
预览

Video captioning is a challenging task that requires a deep understanding of visual scenes. State-of-the-art methods generate captions using either scene-level or object-level information but without explicitly modeling object interactions. Thus, they often fail to make visually grounded predictions, and are sensitive to spurious correlations. In this paper, we propose a novel spatio-temporal graph model for video captioning that exploits object interactions in space and time. Our model builds interpretable links and is able to provide explicit visual grounding. To avoid unstable performance caused by the variable number of objects, we further propose an object-aware knowledge distillation mechanism, in which local object information is used to regularize global scene features. We demonstrate the efficacy of our approach through extensive experiments on two benchmarks, showing our approach yields competitive performance with interpretable predictions.

0
17
下载
预览

Existing knowledge distillation methods focus on convolutional neural networks~(CNNs), where the input samples like images lie in a grid domain, and have largely overlooked graph convolutional networks~(GCN) that handle non-grid data. In this paper, we propose to our best knowledge the first dedicated approach to {distilling} knowledge from a pre-trained GCN model. To enable the knowledge transfer from the teacher GCN to the student, we propose a local structure preserving module that explicitly accounts for the topological semantics of the teacher. In this module, the local structure information from both the teacher and the student are extracted as distributions, and hence minimizing the distance between these distributions enables topology-aware knowledge transfer from the teacher, yielding a compact yet high-performance student model. Moreover, the proposed approach is readily extendable to dynamic graph models, where the input graphs for the teacher and the student may differ. We evaluate the proposed method on two different datasets using GCN models of different architectures, and demonstrate that our method achieves the state-of-the-art knowledge distillation performance for GCN models.

0
16
下载
预览

Neural network models usually suffer from the challenge of incorporating commonsense knowledge into the open-domain dialogue systems. In this paper, we propose a novel knowledge-aware dialogue generation model (called TransDG), which transfers question representation and knowledge matching abilities from knowledge base question answering (KBQA) task to facilitate the utterance understanding and factual knowledge selection for dialogue generation. In addition, we propose a response guiding attention and a multi-step decoding strategy to steer our model to focus on relevant features for response generation. Experiments on two benchmark datasets demonstrate that our model has robust superiority over compared methods in generating informative and fluent dialogues. Our code is available at https://github.com/siat-nlp/TransDG.

0
12
下载
预览

Knowledge distillation is typically conducted by training a small model (the student) to mimic a large and cumbersome model (the teacher). The idea is to compress the knowledge from the teacher by using its output probabilities as soft-labels to optimize the student. However, when the teacher is considerably large, there is no guarantee that the internal knowledge of the teacher will be transferred into the student; even if the student closely matches the soft-labels, its internal representations may be considerably different. This internal mismatch can undermine the generalization capabilities originally intended to be transferred from the teacher to the student. In this paper, we propose to distill the internal representations of a large model such as BERT into a simplified version of it. We formulate two ways to distill such representations and various algorithms to conduct the distillation. We experiment with datasets from the GLUE benchmark and consistently show that adding knowledge distillation from internal representations is a more powerful method than only using soft-label distillation.

0
4
下载
预览

We introduce an adversarial learning framework, which we named KBGAN, to improve the performances of a wide range of existing knowledge graph embedding models. Because knowledge graph datasets typically only contain positive facts, sampling useful negative training examples is a non-trivial task. Replacing the head or tail entity of a fact with a uniformly randomly selected entity is a conventional method for generating negative facts used by many previous works, but the majority of negative facts generated in this way can be easily discriminated from positive facts, and will contribute little towards the training. Inspired by generative adversarial networks (GANs), we use one knowledge graph embedding model as a negative sample generator to assist the training of our desired model, which acts as the discriminator in GANs. The objective of the generator is to generate difficult negative samples that can maximize their likeliness determined by the discriminator, while the discriminator minimizes its training loss. This framework is independent of the concrete form of generator and discriminator, and therefore can utilize a wide variety of knowledge graph embedding models as its building blocks. In experiments, we adversarially train two translation-based models, TransE and TransD, each with assistance from one of the two probability-based models, DistMult and ComplEx. We evaluate the performances of KBGAN on the link prediction task, using three knowledge base completion datasets: FB15k-237, WN18 and WN18RR. Experimental results show that adversarial training substantially improves the performances of target embedding models under various settings.

0
5
下载
预览
小贴士
相关论文
Junjie Hu,Chenyou Fan,Hualie Jiang,Xiyue Guo,Xiangyong Lu,Tin Lun Lam
0+阅读 · 5月13日
Mengqi Xue,Jie Song,Xinchao Wang,Ying Chen,Xingen Wang,Mingli Song
0+阅读 · 5月10日
Xiang Wang,Tinglin Huang,Dingxian Wang,Yancheng Yuan,Zhenguang Liu,Xiangnan He,Tat-Seng Chua
3+阅读 · 2月14日
Progressive Network Grafting for Few-Shot Knowledge Distillation
Chengchao Shen,Xinchao Wang,Youtan Yin,Jie Song,Sihui Luo,Mingli Song
4+阅读 · 2020年12月9日
Spatio-Temporal Graph for Video Captioning with Knowledge Distillation
Boxiao Pan,Haoye Cai,De-An Huang,Kuan-Hui Lee,Adrien Gaidon,Ehsan Adeli,Juan Carlos Niebles
17+阅读 · 2020年3月31日
Distillating Knowledge from Graph Convolutional Networks
Yiding Yang,Jiayan Qiu,Mingli Song,Dacheng Tao,Xinchao Wang
16+阅读 · 2020年3月23日
Jian Wang,Junhao Liu,Wei Bi,Xiaojiang Liu,Kejing He,Ruifeng Xu,Min Yang
12+阅读 · 2019年12月16日
Knowledge Distillation from Internal Representations
Gustavo Aguilar,Yuan Ling,Yu Zhang,Benjamin Yao,Xing Fan,Edward Guo
4+阅读 · 2019年10月8日
Liwei Cai,William Yang Wang
5+阅读 · 2017年11月11日
相关资讯
17篇必看[知识图谱Knowledge Graphs] 论文@AAAI2020
Transferring Knowledge across Learning Processes
CreateAMind
6+阅读 · 2019年5月18日
人工智能 | NIPS 2019等国际会议信息8条
Call4Papers
6+阅读 · 2019年3月21日
IEEE | DSC 2019诚邀稿件 (EI检索)
Call4Papers
6+阅读 · 2019年2月25日
DiscuzX 3.4 Phar反序列化漏洞
黑客工具箱
7+阅读 · 2019年1月4日
disentangled-representation-papers
CreateAMind
20+阅读 · 2018年9月12日
Hierarchical Disentangled Representations
CreateAMind
3+阅读 · 2018年4月15日
人工智能 | 国际会议截稿信息9条
Call4Papers
4+阅读 · 2018年3月13日
论文浅尝 | Open world Knowledge Graph Completion
开放知识图谱
7+阅读 · 2018年1月30日
Top