Awesome Knowledge Distillation

Collection of papers on Knowledge Distillation. The PDF of each paper could be obtained by clicking the title.

Paper list

1. Spatial knowledge distillation to aid visual reasoning. Aditya, S., Saha, R., Yang, Y. & Baral, C. (2019). WACV.
2. Knowledge distillation from internal representations. Aguilar, G., Ling, Y., Zhang, Y., Yao, B., Fan, X. & Guo, E. (2020). AAAI.
3. Compressing gans using knowledge distillation. Aguinaldo, A., Chiang, P. Y., Gain, A., Patil, A., Pearson, K. & Feizi, S. (2019).
4. Variational information distillation for knowledge transfer. Ahn, S., Hu, S., Damianou, A., Lawrence, N. D. & Dai, Z. (2019). CVPR.
5. Emotion recognition in speech using crossmodal transfer in the wild. Albanie, S., Nagrani, A., Vedaldi, A. & Zisserman, A. (2018). ACM MM.
6. Learning and generalization in overparameterized neural networks going beyond two layers. Allen-Zhu, Z., Li, Y., & Liang, Y. (2019). NeurIPS.
7. Large scale distributed neural network training through online distillation. Anil, R., Pereyra, G., Passos, A., Ormandi, R., Dahl, G. E.. & Hinton, G. E. (2018). ICLR.
8. On the optimization of deep networks: Implicit acceleration by overparameterization. Arora, S., Cohen, N., & Hazan, E. (2018). ICML.
9. On knowledge distillation from complex networks for response prediction. Arora, S., Khapra, M. M. & Ramaswamy, H. G. (2019). NAACL-HLT.
10. Domain adaptation of dnn acoustic models using knowledge distillation. Asami, T., Masumura, R., Yamaguchi, Y., Masataki, H. & Aono, Y. (2017). ICASSP.
11. N2N learning: Network to network compression via policy gradient reinforcement learning. Ashok, A., Rhinehart, N., Beainy, F. & Kitani, K. M. (2018). ICLR.
12. Ensemble knowledge distillation for learning improved and efficient networks. Asif, U., Tang, J. & Harrer, S. (2020). ECAI.
13. Do deep nets really need to be deep?. Ba, J. & Caruana, R. (2014). NeurIPS.
14. Label refinery: Improving imagenet classification through label progressio. Bagherinezhad, H., Horton, M., Rastegari, M. & Farhadi, A. (2018).
15. Few shot network compression via cross distillation. Bai, H., Wu, J., King, I. & Lyu, M. (2020). AAAI.
16. Learn spelling from teachers: transferring knowledge from language models to sequence-to-sequence speech recognition. Bai, Y., Yi, J., Tao, J., Tian, Z. &Wen, Z. (2019). Interspeech.
17. Teacher guided architecture search. Bashivan, P., Tensen, M. & DiCarlo, J. J. (2019). ICCV.
18. Adversarial network compression. Belagiannis, V., Farshad, A. & Galasso, F. (2018). ECCV.
19. Representation learning: A review and new perspectives. Bengio, Y., Courville, A., & Vincent, P. (2013). IEEE TPAMI 35(8): 1798–1828.
20. Uninformed students: Student-teacher anomaly detection with discriminative latent embeddings. Bergmann, P., Fauser, M., Sattlegger, D., & Steger, C. (2020). CVPR.
21. Efficient video classification using fewer frames. Bhardwaj, S., Srinivasan, M. & Khapra, M. M. (2019). CVPR.
22. Distributed Distillation for On-Device Learning. Bistritz, I., Mann, A., & Bambos, N. (2020). NeurIPS.
23. Flexible Dataset Distillation: Learn Labels Instead of Images. Bohdal, O., Yang, Y., & Hospedales, T. (2020).
24. Stochastic Precision Ensemble: Self-Knowledge Distillation for Quantized Deep Neural Networks. Boo, Y., Shin, S., Choi, J., & Sung, W. (2021). AAAI.
25. Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem. Brutzkus, A., & Globerson, A. (2019). ICML.
26. Model compression. Bucilua, C., Caruana, R. & Niculescu-Mizil, A. (2006). SIGKDD.
27. Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning. Caccia, M., Rodriguez, P., Ostapenko, O., Normandin, F., Lin, M., Caccia, L., Laradji, I., Rish, I., Lacoste, A., Vazquez D., & Charlin, L. (2020). NeurIPS.
28. Transferring knowledge from a rnn to a DNN. Chan, W., Ke, N. R. & Lane, I. (2015).
29. Data-Free Knowledge Distillation for Object Detection. Chawla, A., Yin, H., Molchanov, P., & Alvarez, J. (2021). WACV.
30. Distilling knowledge from ensembles of neural networks for speech recognition. Chebotar, Y. & Waters, A. (2016). Interspeech.
31. Online knowledge distillation with diverse peers. Chen, D., Mei, J. P., Wang, C., Feng, Y. & Chen, C. (2020a). AAAI.
32. Cross-Layer Distillation with Semantic Calibration. Chen, D., Mei, J. P., Zhang, Y., Wang, C., Wang, Z., Feng, Y., & Chen, C. (2021). AAAI.
33. Learning efficient object detection models with knowledge distillation. Chen, G., Choi, W., Yu, X., Han, T., & Chandraker, M. (2017). NeurIPS.
34. Data-Free Learning of Student Networks. Chen, H., Wang, Y., Xu, C., Yang, Z., Liu, C., Shi, B., Xu, C., Xu, C., &Tian, Q. (2019a). ICCV.
35. Learning student networks via feature embedding. Chen, H., Wang, Y., Xu, C., Xu, C. & Tao, D. (2021). IEEE TNNLS 32(1): 25-35.
36. Net2Net: ACCELERATING LEARNING VIA KNOWLEDGE TRANSFER. Chen, T., Goodfellow, I. & Shlens, J. (2016). ICLR.
37. Knowledge distillation with feature maps for image classification. Chen, W. C., Chang, C. C. & Lee, C. R. (2018a). ACCV.
38. Adversarial distillation for efficient recommendation with external knowledge. Chen, X., Zhang, Y., Xu, H., Qin, Z. & Zha, H. (2018b). ACM TOIS 37(1): 1–28.
39. A two-teacher tramework for knowledge distillation. Chen, X., Su, J. & Zhang, J. (2019b). ISNN.
40. Darkrank: Accelerating deep metric learning via cross sample similarities transfer. Chen, Y., Wang, N. & Zhang, Z. (2018c). AAAI.
41. Distilling knowledge learned in BERT for text generation. Chen, Y. C., Gan, Z., Cheng, Y., Liu, J., & Liu, J. (2020b). ACL.
42. Crdoco: Pixel-level domain transfer with cross-domain consistency. Chen, Y. C., Lin, Y. Y., Yang, M. H., Huang, J. B. (2019c). CVPR.
43. Lifelong Machine Learning, Second Edition Synthesis Lectures on Artificial Intelligence and Machine Learning. Chen, Z. & Liu, B. (2018). 12(3): 1–207.
44. A Multi-task Mean Teacher for Semi-supervised Shadow Detection. Chen, Z., Zhu, L., Wan, L., Wang, S., Feng, W., & Heng, P. A. (2020c). CVPR.
45. Model compression and acceleration for deep neural networks: The principles, progress, and challenges. Cheng, Y., Wang, D., Zhou, P. & Zhang, T. (2018). IEEE Signal Proc Mag 35(1): 126–136.
46. Explaining Knowledge Distillation by Quantifying the Knowledge. Cheng, X., Rao, Z., Chen, Y., & Zhang, Q. (2020). CVPR.
47. On the efficacy of knowledge distillation. Cho, J. H. & Hariharan, B. (2019). ICCV.
48. Xception: Deep learning with depthwise separable convolutions. Chollet, F. (2017). CVPR.
49. Feature-map-level online adversarial knowledge distillation. Chung, I., Park, S., Kim, J. & Kwak, N. (2020). ICML.
50. Bam! born-again multitask networks for natural language understanding. Clark, K., Luong, M. T., Khandelwal, U., Manning, C. D. & Le, Q. V. (2019). ACL. 51.Binaryconnect: Training deep neural networks with binary weights during propagations. Courbariaux, M., Bengio, Y. & David, J. P. (2015). NeurIPS. 52.Moonshine: Distilling with cheap convolutions. Crowley, E. J., Gray, G. & Storkey, A. J. (2018). NeurIPS.
51. Knowledge distillation across ensembles of multilingual models or low-resource languages. Cui, J., Kingsbury, B., Ramabhadran, B., Saon, G., Sercu, T., Audhkhasi, K. & et al. (2017).ICASSP.
52. Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition. Cui, Z., Song, T., Wang, Y., & Ji, Q. (2020). NeurIPS.
53. Defocus Blur Detection via Depth Distillation. Cun, X., & Pun, C. M. (2020). ECCV.
54. ImageNet: A large-scale hierarchical image database. Deng, J., Dong, W., Socher, R., Li, L. J., Li, K., & Fei- Fei, L. (2009). CVPR.
55. Exploiting linear structure within convolutional networks for efficient evaluation. Denton, E. L., Zaremba, W., Bruna, J., LeCun, Y. & Fergus, R. (2014).NeurIPS.
56. Bert: Pre-training of deep bidirectional transformers for language understanding Devlin, J., Chang, M. W., Lee, K. & Toutanova, K. (2019). NAACL-HLT.
57. Adaptive regularization of labels Ding, Q., Wu, S., Sun, H., Guo, J. & Xia, ST. (2019).
58. Compact trilinear interaction for visual question answering Do, T., Do, T. T., Tran, H., Tjiputra, E. & Tran, Q. D. (2019). ICCV.
59. Teacher supervises students how to learn from partially labeled images for facial landmark detection Dong, X. & Yang, Y. (2019). ICCV.
60. Unpaired multi-modal segmentation via knowledge distillation Dou, Q., Liu, Q., Heng, P. A., & Glocker, B. (2020). IEEE TMI
61. Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space Du, S., You, S., Li, X., Wu, J., Wang, F., Qian, C., & Zhang, C. (2020). NeurIPS.
62. ShrinkTeaNet: Million-scale lightweight face recognition via shrinking teacher-student networks Duong, C. N., Luu, K., Quach, K. G. & Le, N. (2019.)
63. Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation Fakoor, R., Mueller, J. W., Erickson, N., Chaudhari, P., & Smola, A. J. (2020). NeurIPS.
64. Transferring knowledge across learning processes Flennerhag, S., Moreno, P. G., Lawrence, N. D. & Damianou, A. (2019). ICLR.
65. Ensemble distillation for neural machine translation Freitag, M., Al-Onaizan, Y. & Sankaran, B. (2017).
66. LRC-BERT: Latent representation Contrastive Knowledge Distillation for Natural Language Understanding. Fu, H., Zhou, S., Yang, Q., Tang, J., Liu, G., Liu, K., & Li, X. (2021). AAAI.
67. Efficient knowledge distillation from an ensemble of teachers. Fukuda, T., Suzuki, M., Kurata, G., Thomas, S., Cui, J. & Ramabhadran, B. (2017). Interspeech.
68. Born again neural networks. Furlanello, T., Lipton, Z., Tschannen, M., Itti, L. & Anandkumar, A. (2018). ICML.
69. An adversarial feature distillation method for audio classification. Gao, L., Mi, H., Zhu, B., Feng, D., Li, Y. & Peng, Y. (2019). IEEE Access 7: 105319–105330.
70. Residual Error Based Knowledge Distillation. Gao, M., Wang, Y., & Wan, L. (2021). Neurocomputing 433: 154-161.
71. Privileged modality distillation for vessel border detection in intracoronary imaging. Gao, Z., Chung, J., Abdelrazek, M., Leung, S., Hau,W. K., Xian, Z., Zhang, H., & Li, S. (2020). IEEE TMI 39(5): 1524-1534.
72. Modality distillation with multiple stream networks for action recognition. Garcia, N. C., Morerio, P. & Murino, V. (2018). ECCV.
73. Low-resolution face recognition in the wild via selective knowledge distillation. Ge, S., Zhao, S., Li, C. & Li, J. (2018). IEEE TIP 28(4):2051–2062.
74. Efficient Low-Resolution Face Recognition via Bridge Distillation. Ge, S., Zhao, S., Li, C., Zhang, Y., & Li, J. (2020). IEEE TIP 29: 6898-6908.
75. Advancing multi-accented lstm-ctc speech recognition using a domain specific student-teacher learning paradigm. Ghorbani, S., Bulut, A. E. & Hansen, J. H. (2018). SLTW.
76. White-to-black: Efficient distillation of black-box adversarial attacks. Gil, Y., Chai, Y., Gorodissky, O. & Berant, J. (2019). NAACL-HLT.
77. Adversarially robust distillation. Goldblum, M., Fowl, L., Feizi, S. & Goldstein, T. (2020). AAAI.
78. Teaching semi-supervised classifier via generalized distillation. Gong, C., Chang, X., Fang, M. & Yang, J. (2018). IJCAI.
79. Label propagation via teaching-to-learn and learningto-teach. Gong, C., Tao, D., Liu, W., Liu, L., & Yang, J. (2017). TNNLS 28(6): 1452–1465.
80. Generative adversarial nets. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., & Bengio, Y. (2014). NeurIPS.
81. Explaining sequencelevel knowledge distillation as data-augmentation for neural machine translation. Gordon, M. A. & Duh, K. (2019).
82. Search for Better Students to Learn Distilled Knowledge. Gu, J., & Tresp, V. (2020). ECAI.
83. Differentiable Feature Aggregation Search for Knowledge Distillation. Guan, Y., Zhao, P., Wang, B., Zhang, Y., Yao, C., Bian, K., & Tang, J. (2020). ECCV.
84. Online Knowledge Distillation via Collaborative Learning. Guo, Q., Wang, X., Wu, Y., Yu, Z., Liang, D., Hu, X., & Luo, P. (2020). CVPR.
85. Cross modal istillation for supervision transfer. Gupta, S., Hoffman, J. & Malik, J. (2016). CVPR.
86. Self-knowledge distillation in natural language processing. Hahn, S. & Choi, H. (2019). RANLP.
87. Textkdgan: Text generation using knowledge distillation and generative adversarial networks. Haidar, M. A. & Rezagholizadeh, M. (2019). Canadian Conference on Artificial Intelligence.
88. Learning both weights and connections for efficient neural network. Han, S., Pool, J., Tran, J. & Dally, W. (2015). NeurIPS.
89. Spatiotemporal distilled dense-connectivity network for video action recognition. Hao, W. & Zhang, Z. (2019). Pattern Recogn 92: 13–24.
90. The knowledge within: Methods for data-free model compression. Haroush, M., Hubara, I., Hoffer, E., & Soudry, D. (2020). CVPR.
91. Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge. He, C., Annavaram, M., & Avestimehr, S. (2020a). NeurIPS.
92. Why resnet works? residuals generalize. He, F., Liu, T., & Tao, D. (2020b). IEEE TNNLS 31(12): 5349–5362.
93. Deep residual learning for image recognition. He, K., Zhang, X., Ren, S. & Sun, J. (2016). CVPR.
94. Knowledge adaptation for efficient semantic segmentation. He, T., Shen, C., Tian, Z., Gong, D., Sun, C. & Yan, Y. (2019). CVPR.
95. A comprehensive overhaul of feature distillation. Heo, B., Kim, J., Yun, S., Park, H., Kwak, N., & Choi, J. Y. (2019a). ICCV.
96. Knowledge distillation with adversarial samples supporting decision boundary. Heo, B., Lee, M., Yun, S. & Choi, J. Y. (2019b). AAAI.
97. Knowledge transfer via distillation of activation boundaries formed by hidden neurons. Heo, B., Lee, M., Yun, S. & Choi, J. Y.(2019c). AAAI.
98. Distilling the knowledge in a neural network. Hinton, G., Vinyals, O. & Dean, J. (2015).
99. Learning with Side Information through Modality Hallucination.Hoffman, J., Gupta, S. & Darrell, T. (2016). CVPR.
100. GAN-Knowledge Distillation for one-stage Object Detection. Hong, W. & Yu, J. (2019).
101. Learning lightweight lane detection cnns by self attention distillation. Hou, Y., Ma, Z., Liu, C. & Loy, CC. (2019). ICCV.
102. Inter-Region Affinity Distillation for Road Marking Segmentation. Hou, Y., Ma, Z., Liu, C., Hui, T. W., & Loy, C. C.(2020). CVPR.
103. Mobilenets: Efficient convolutional neural networks for mobile vision applications. Howard, A. G., Zhu, M., Chen, B., Kalenichenko, D., Wang, W., Weyand, T., Andreetto, M., & Adam, H. (2017).
104. Creating Something from Nothing: Unsupervised Knowledge Distillation for Cross-Modal Hashing. Hu, H., Xie, L., Hong, R., & Tian, Q. (2020). CVPR.
105. Attention-guided answer distillation for machine reading comprehension. Hu, M., Peng, Y., Wei, F., Huang, Z., Li, D., Yang, N. & et al. (2018). EMNLP.
106. Densely connected convolutional networks. Huang, G., Liu, Z., Van, Der Maaten, L. & Weinberger, K. Q. (2017). CVPR.
107. Knowledge Distillation for Sequence Model. Huang, M., You, Y., Chen, Z., Qian, Y. & Yu, K. (2018). Interspeech.
108. Like What You Like: Knowledge Distill via Neuron Selectivity Transfer. Huang, Z. & Wang, N. (2017).
109. Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection. Huang, Z., Zou, Y., Bhagavatula, V., & Huang, D. (2020). NeurIPS.
110. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Ioffe, S., & Szegedy, C. (2015). ICML
111. Learning what and where to transfer. Jang, Y., Lee, H., Hwang, S. J. & Shin, J. (2019). ICML.
112. Knowledge Distillation in Wide Neural Networks:Risk Bound, Data Efficiency and Imperfect Teacher. Ji, G., & Zhu, Z. (2020). NeurIPS.
113. Tinybert: Distilling bert for natural language understanding. Jiao, X., Yin, Y., Shang, L., Jiang, X., Chen, X., Li, L. & et al. (2020). EMNLP.
114. Knowledge distillation via route constrained optimization. Jin, X., Peng, B., Wu, Y., Liu, Y., Liu, J., Liang, D., Yan, J. & Hu, X. (2019). ICCV.
115. Towards oracle knowledge distillation with neural architecture search. Kang, M., Mun, J. & Han, B. (2020). AAAI.
116. Paraphrasing Complex Network: Network Compression via Factor Transfer. Kim, J., Park, S. & Kwak, N. (2018). NeurIPS.
117. QKD: Quantization-aware Knowledge Distillation. Kim, J., Bhalgat, Y., Lee, J., Patel, C., & Kwak, N. (2019a).
118. Feature fusion for online mutual knowledge distillation. Kim, J., Hyun, M., Chung, I. & Kwak, N. (2019b). ICPR.
119. TRANSFERRING KNOWLEDGE TO SMALLER NETWORK WITH CLASS-DISTANCE LOSS.Kim, S. W. & Kim, H. E. (2017). ICLRW.
120. Sequence-Level Knowledge Distillation. Kim, Y., Rush & A. M. (2016). EMNLP.
121. Few-shot learning of neural networks from scratch by pseudo example optimization. Kimura, A., Ghahramani, Z., Takeuchi, K., Iwata, T. & Ueda, N. (2018). BMVC.
122. ADAPTIVE KNOWLEDGE DISTILLATION BASED ON ENTROPY. Kwon, K., Na, H., Lee, H., & Kim, N. S. (2020). ICASSP.
123. Cross-Resolution Face Recognition via Prior-Aided Face Hallucination and Residual Knowledge Distillation. Kong, H., Zhao, J., Tu, X., Xing, J., Shen, S. & Feng, J. (2019).
124. Learning multiple layers of features from tiny images.
125. Imagenet classification with deep convolutional neural networks. Krizhevsky, A., Sutskever, I. & Hinton, G. E. (2012). NeurIPS.
126. Distilling an Ensemble of Greedy Dependency Parsers into One MST Parser. Kuncoro, A., Ballesteros, M., Kong, L., Dyer, C. & Smith, N. A. (2016). EMNLP.
127. Unsupervised multi-task adaptation using adversarial cross-task distillation. Kundu, J. N., Lakkakula, N. & Babu, R. V. (2019). CVPR.
128. Dual Policy Distillation. Lai, K. H., Zha, D., Li, Y., & Hu, X. (2020). IJCAI.
129. Self-Referenced Deep Learning. Lan, X., Zhu, X., & Gong, S. (2018). ACCV.
130. Rethinking data augmentation: Self-supervision and selfdistillation. Lee, H., Hwang, S. J. & Shin, J. (2019a).
131. Overcoming catastrophic forgetting with unlabeled data in the wild. Lee, K., Lee, K., Shin, J. & Lee, H. (2019b). ICCV.
132. Stochasticity and Skip Connection Improve Knowledge Transfer. Lee, K., Nguyen, L. T. & Shim, B. (2019c). AAAI.
133. Graph-based knowledge distillation by multi-head attention network. Lee, S. & Song, B. (2019). BMVC.
134. Selfsupervised knowledge distillation using singular value decomposition. Lee, S. H., Kim, D. H. & Song, B. C. (2018). ECCV.
135. Learning Light-Weight Translation Models from Deep Transformer. Li, B., Wang, Z., Liu, H., Du, Q., Xiao, T., Zhang, C., & Zhu, J. (2021). AAAI.
136. Blockwisely Supervised Neural Architecture Search with Knowledge Distillation.. Li, C., Peng, J., Yuan, L., Wang, G., Liang, X., Lin, L.,& Chang, X. (2020a). CVPR.
137. Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts. Li, G., Zhang, J., Wang, Y., Liu, C., Tan, M., Lin, Y., Zhang, W., Feng, J., & Zhang, T. (2020b). NeurIPS.
138. Spatiotemporal knowledge distillation for efficient estimation of aerial video saliency. Li, J., Fu, K., Zhao, S. & Ge, S. (2019). IEEE TIP 29:1902–1914.
139. Gan compression: Efficient architectures for interactive conditional gans. Li, M., Lin, J., Ding, Y., Liu, Z., Zhu, J. Y., & Han, S.(2020c). CVPR.
140. Mimicking very efficient network for object detection. Li, Q., Jin, S. & Yan, J. (2017). CVPR.
141. Few sample knowledge distillation for efficient network compression. Li, T., Li, J., Liu, Z., & Zhang, C. (2020d). CVPR.
142. Local Correlation Consistency for Knowledge Distillation. Li, X., Wu, J., Fang, H., Liao, Y., Wang, F., & Qian, C. (2020e). ECCV.
143. Learning without forgetting. Li, Z. & Hoiem, D. (2017).IEEE TPAMI 40(12): 2935–2947.
144. Ensemble distillation for robust model fusion in federated learning. Lin, T., Kong, L., Stich, S. U., & Jaggi, M. (2020). NeurIPS.
145. Knowledge flow:Improve upon your teachers. Liu, I. J., Peng, J. & Schwing, A. G. (2019a). ICLR.
146. Exploiting the ground-truth: An adversarial imitation based knowledge distillation approach for event detection. Liu, J., Chen, Y. & Liu, K. (2019b). AAAI.
147. Knowledge representing:efficient, sparse representation of prior knowledge for knowledge distillation. Liu, J., Wen, D., Gao, H., Tao, W., Chen, T. W., Osa, K. & et al. (2019c). CVPRW.
148. DDFlow: Learning optical flow with unlabeled data distillation. Liu, P., King, I., Lyu, M. R., & Xu, J. (2019d). AAAI.
149. Ktan: knowledge transfer adversarial network. Liu, P., Liu, W., Ma, H., Mei, T. & Seok, M. (2020a). IJCNN.
150. Semantic-aware knowledge preservation for zero-shot sketch-based image retrieval. Liu, Q., Xie, L., Wang, H., Yuille & A. L. (2019e). . ICCV.
151. Model compression with generative adversarial networks. Liu, R., Fusi, N. & Mackey, L. (2018).
152. FastBERT: a self-distilling BERT with Adaptive Inference Time. Liu, W., Zhou, P., Zhao, Z., Wang, Z., Deng, H., & Ju,Q. (2020b). ACL.
153. Improving the interpretability of deep neural networks with knowledge distillation. Liu, X., Wang, X. & Matwin, S. (2018b). ICDMW.
154. Improving multi-task deep neural networks via knowledge distillation for natural language understanding. Liu, X., He, P., Chen, W. & Gao, J. (2019f).
155. Knowledge distillation via instance relationship graph. Liu, Y., Cao, J., Li, B., Yuan, C., Hu, W., Li, Y. & Duan, Y. (2019g). CVPR.
156. Structured knowledge distillation for semantic segmentation. Liu, Y., Chen, K., Liu, C., Qin, Z., Luo, Z. & Wang, J. (2019h). CVPR.
157. Search to distill: Pearls are everywhere but not the eyes. Liu, Y., Jia, X., Tan, M., Vemulapalli, R., Zhu, Y., Green, B. & et al. (2019i). CVPR.
158. Adaptive multi-teacher multi-level knowledge distillation. Liu, Y., Zhang, W., & Wang, J. (2020c). Neu-rocomputing 415: 106-113.
159. Data-free knowledge distillation for deep neural networks. Lopes, R. G., Fenu, S. & Starner, T. (2017). NeurIPS.
160. Unifying distillation and privileged information. Lopez-Paz, D., Bottou, L., Sch¨olkopf, B. & Vapnik, V. (2016). ICLR.
161. Knowledge distillation for small-footprint highway networks. Lu, L., Guo, M. & Renals, S. (2017). ICASSP.
162. Face model compression by distilling knowledge from neurons. Luo, P., Zhu, Z., Liu, Z., Wang, X. & Tang, X. (2016). AAAI.
163. Collaboration by Competition: Selfcoordinated Knowledge Amalgamation for Multitalent Student Learning. Luo, S., Pan, W., Wang, X., Wang, D., Tang, H., & Song, M. (2020). ECCV.
164. Knowledge amalgamation from heterogeneous networks by common feature learning. Luo, S., Wang, X., Fang, G., Hu, Y., Tao, D., & Song, M. (2019). IJCAI.
165. Graph distillation for action detection with privileged modalities. Luo, Z., Hsieh, J. T., Jiang, L., Carlos Niebles, J.& Fei- Fei, L. (2018). ECCV.
166. Improving neural architecture search image classifiers via ensemble learning. Macko, V., Weill, C., Mazzawi, H. & Gonzalvo, J. (2019). NeurIPS Workshop.
167. Graph representation learning via multi-task knowledge distillation. Ma, J., & Mei, Q. (2019).
168. Shufflenet v2: Practical guidelines for efficient cnn architecture design. Ma, N., Zhang, X., Zheng, H. T., & Sun, J. (2018). ECCV
169. Conditional teacher-student learning. Meng, Z., Li, J., Zhao, Y. & Gong, Y. (2019). ICASSP.
170. Zero-shot Knowledge Transfer via Adversarial Belief Matching. Micaelli, P. & Storkey, A. J. (2019). NeurIPS.
171. Knowledge transfer graph for deep collaborative learning. Minami, S., Hirakawa, T., Yamashita, T. & Fujiyoshi, H. (2019).
172. Improved knowledge distillation via teacher assistant. Mirzadeh, S. I., Farajtabar,M., Li, A. & Ghasemzadeh, H. (2020). AAAI.
173. Apprentice: Using knowledge distillation techniques to improve lowprecision network accuracy. Mishra, A. & Marr, D. (2018). ICLR.
174. Self-distillation amplifies regularization in hilbert space. Mobahi, H., Farajtabar, M., & Bartlett, P. L. (2020). NeurIPS.
175. Distilling word embeddings: An encoding approach. Mou, L., Jia, R., Xu, Y., Li, G., Zhang, L. & Jin, Z. (2016). CIKM.
176. Cogni-net: Cognitive feature learning through deep visual perception. Mukherjee, P., Das, A., Bhunia, A. K. & Roy, P. P. (2019). ICIP.
177. Online model distillation for efficient video inference. Mullapudi, R. T., Chen, S., Zhang, K., Ramanan, D. & Fatahalian, K. (2019). ICCV.
178. When does label smoothing help?. Muller, R., Kornblith, S. & Hinton, G. E. (2019). NeurIPS.
179. Learning to specialize with knowledge distillation for visual question answering. Mun, J., Lee, K., Shin, J. & Han, B. (2018). NeurIPS.
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Acknowledgement:

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