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Self-Paced Learning with Adaptive Deep Visual Embeddings [article]

Vithursan Thangarasa, Graham W. Taylor
2018 arXiv   pre-print
In this paper, we introduce Self-Paced Learning with Adaptive Deep Visual Embeddings (SPL-ADVisE), a novel end-to-end training protocol that unites self-paced learning (SPL) and deep metric learning (DML  ...  We leverage the Magnet Loss to train an embedding convolutional neural network (CNN) to learn a salient representation space.  ...  We exploit a new type of knowledge -similar instance-level samples are discovered through an embedding network trained by DML concurrently with the self-paced learner.  ... 
arXiv:1807.09200v1 fatcat:jpbmfbbeg5hsle7h37x5iukxz4

Self Paced Adversarial Training for Multimodal Few-shot Learning [article]

Frederik Pahde, Oleksiy Ostapenko, Patrick Jähnichen, Tassilo Klein, Moin Nabi
2018 arXiv   pre-print
In this regard, we propose a self-paced class-discriminative generative adversarial network incorporating multimodality in the context of few-shot learning.  ...  We improve few-shot learning accuracies on the finegrained CUB and Oxford-102 datasets.  ...  Curriculum learning was extended to self-paced learning by Kumar et al. [14] .  ... 
arXiv:1811.09192v1 fatcat:6msho45ygngchbwgbv4bb2zn64

Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation [article]

Zhizhe Liu, Zhenfeng Zhu, Shuai Zheng, Yang Liu, Jiayu Zhou, Yao Zhao
2021 arXiv   pre-print
To enhance the supervision for contrastive learning, more informative pseudo-labels are generated in target domain in a self-paced way, thus benefiting the category-aware distribution alignment for UDA  ...  To address this issue, we propose in this paper a novel margin preserving self-paced contrastive Learning (MPSCL) model for cross-modal medical image segmentation.  ...  To avoid selecting pixels with error-prone predictions, we propose a self-paced pseudo-labels assigning approach in the embedding space, which is mainly based on the assumption that the well-adapted pixel  ... 
arXiv:2103.08454v1 fatcat:i7dahzzs6zcabeco4v5zjtqyay

PACE: A Parallelizable Computation Encoder for Directed Acyclic Graphs [article]

Zehao Dong, Muhan Zhang, Fuhai Li, Yixin Chen
2022 arXiv   pre-print
We demonstrate the superiority of PACE through encoder-dependent optimization subroutines that search the optimal DAG structure based on the learned DAG embeddings.  ...  Experiments show that PACE not only improves the effectiveness over previous sequential DAG encoders with a significantly boosted training and inference speed, but also generates smooth latent (DAG encoding  ...  When PACE is trained in a pre-training architecture, similar to the sentiment classification task in BERT, PACE takes the learned embedding of the output node as the DAG encoding.  ... 
arXiv:2203.10304v1 fatcat:qkuei5c7mzeppiy43jgkl57isa

Taxonomy of Cybersecurity Awareness Delivery Methods: A Countermeasure for Phishing Threats

Asma A. Alhashmi, Abdulbasit Darem, Jemal H. Abawajy
2021 International Journal of Advanced Computer Science and Applications  
For organizations conducting or considering phishing training, it helps them understand the various awareness training and phishing campaigns capabilities and design an appropriate program with a meaningful  ...  It is a self-paced learning where the learner can pause the video at any time and re-watch it later.  ...  The side effect of a restriction on game play time is that it can make self-paced learning impossible.  ... 
doi:10.14569/ijacsa.2021.0121004 fatcat:ld3idu3b5nc37klaecopq6vev4

Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence [article]

Chen Ma, Yingxue Zhang, Qinglong Wang, Xue Liu
2018 arXiv   pre-print
To cope with these challenges, we propose a novel autoencoder-based model to learn the non-linear user-POI relations, namely SAE-NAD, which consists of a self-attentive encoder (SAE) and a neighbor-aware  ...  the inner product of POI embeddings together with the radial basis function (RBF) kernel.  ...  Deep learning-based methods: • PACE, preference and context embedding [34] , a deep neural architecture that jointly learns the embeddings of users and POIs to predict both user preference over POIs and  ... 
arXiv:1809.10770v1 fatcat:4jbtumufrfbefc436kzqxufeku

Low-Shot Learning from Imaginary 3D Model [article]

Frederik Pahde, Mihai Puscas, Jannik Wolff, Tassilo Klein, Nicu Sebe, Moin Nabi
2019 arXiv   pre-print
A self-paced learning approach allows for the selection of a diverse set of high-quality images, which facilitates the training of a classifier.  ...  Since the advent of deep learning, neural networks have demonstrated remarkable results in many visual recognition tasks, constantly pushing the limits.  ...  We see that our shallow CNN (trained with self-paced learning) exceeds both baselines.  ... 
arXiv:1901.01868v1 fatcat:xcsg2lkednhs5dt7p2wzie3btu

Self-supervised Contrastive Video-Speech Representation Learning for Ultrasound [article]

Jianbo Jiao, Yifan Cai, Mohammad Alsharid, Lior Drukker, Aris T.Papageorghiou, J. Alison Noble
2020 arXiv   pre-print
Within this framework, we introduce cross-modal contrastive learning and an affinity-aware self-paced learning scheme to enhance correlation modelling.  ...  In this paper, we propose to address the problem of self-supervised representation learning with multi-modal ultrasound video-speech raw data.  ...  Acknowledgements We acknowledge the EPSRC (EP/M013774/1, Project Seebibyte), ERC(ERC-ADG-2015 694581, Project PULSE), and the support of NVIDIA Corporation with the donation of the GPU.  ... 
arXiv:2008.06607v1 fatcat:vmss2jhwi5e3pal3le323wifxy

Self-supervised Video Representation Learning by Pace Prediction [article]

Jiangliu Wang, Jianbo Jiao, Yun-Hui Liu
2020 arXiv   pre-print
This paper addresses the problem of self-supervised video representation learning from a new perspective -- by video pace prediction.  ...  In addition, we further introduce contrastive learning to push the model towards discriminating different paces by maximizing the agreement on similar video content.  ...  Self-supervised Video Representation Learning by Pace Prediction  ... 
arXiv:2008.05861v2 fatcat:6lntftwnvzfrfonw4pg5zarjvm

Auxiliary Learning for Self-Supervised Video Representation via Similarity-based Knowledge Distillation [article]

Amirhossein Dadashzadeh, Alan Whone, Majid Mirmehdi
2022 arXiv   pre-print
Our method deploys a teacher network that iteratively distills its knowledge to the student model by capturing the similarity information between segments of unlabelled video data.  ...  with a significantly smaller amount of video data, e.g.  ...  Here, we show that the similarity information between embedded feature points can be used as implicit knowledge for self-supervised pretraining to learn more generalised representations through pretext  ... 
arXiv:2112.04011v3 fatcat:kqy57av54fe3bcnqn6msje2mou

Truth Inference with a Deep Clustering-based Aggregation Model

Liang Yin, Yunfei Liu, Weinan Zhang, Yong Yu
2020 IEEE Access  
INDEX TERMS Crowdsourcing, truth inference, clustering methods, neural networks, unsupervised learning, machine learning.  ...  A typical algorithm such as learning from crowds learns a classification model with the guide of inferred true labels where true labels are inferred from source labels.  ...  The clustering loss is constructed via a self-paced learning manner, to encourage the current clustering distribution q q q to approach a self-paced target distribution s s s, which is implemented by the  ... 
doi:10.1109/access.2020.2964484 fatcat:fismxfy6avhsllrufd4xddvofe

Temporal Cycle-Consistency Learning [article]

Debidatta Dwibedi, Yusuf Aytar, Jonathan Tompson, Pierre Sermanet, Andrew Zisserman
2019 arXiv   pre-print
of self-supervised learning in videos, such as Shuffle and Learn and Time-Contrastive Networks.  ...  We introduce a self-supervised representation learning method based on the task of temporal alignment between videos.  ...  We are also grateful to Sourish Chaudhuri for his help with the data collection and Alexandre Passos, Allen Lavoie, Bryan Seybold, and Priya Gupta for their help with the infrastructure.  ... 
arXiv:1904.07846v1 fatcat:oytx6gdzgzbmnb527jt2csbnvi

Self-directed Machine Learning [article]

Wenwu Zhu, Xin Wang, Pengtao Xie
2022 arXiv   pre-print
Specifically, we design SDML as a self-directed learning process guided by self-awareness, including internal awareness and external awareness.  ...  Our proposed SDML process benefits from self task selection, self data selection, self model selection, self optimization strategy selection and self evaluation metric selection through self-awareness  ...  Humans usually choose suitable materials to learn for the given problem, and SDML should have the similar ability to select proper data to learn with awareness of the ultimate goal, the selected task and  ... 
arXiv:2201.01289v2 fatcat:fwzupwnpqzgt7agux7nkrtntxq

Privacy-Preserving Personalized Fitness Recommender System (P3FitRec): A Multi-level Deep Learning Approach [article]

Xiao Liu, Bonan Gao, Basem Suleiman, Han You, Zisu Ma, Yu Liu, Ali Anaissi
2022 arXiv   pre-print
Recommender systems have been successfully used in many domains with the help of machine learning algorithms.  ...  In this paper, we propose a novel privacy-aware personalized fitness recommender system.  ...  Finally, pacing strategy recommendation is achieved by using the pacing strategies from these successful marathon finishers with similar user profiles.  ... 
arXiv:2203.12200v1 fatcat:ctcb2t26djbehcm3uyhuf4fd64

2020 Index IEEE Transactions on Knowledge and Data Engineering Vol. 32

2021 IEEE Transactions on Knowledge and Data Engineering  
Yang, Z., +, TKDE Feb. 2020 203-217 Majdara, A., +, TKDE April 2020 821-826 F Feature extraction Adaptive Self-Paced Deep Clustering with Data Augmentation.  ...  ., +, TKDE April 2020 754-767 Similarity Join and Similarity Self-Join Size Estimation in a Streaming Envi- ronment.  ... 
doi:10.1109/tkde.2020.3038549 fatcat:75f5fmdrpjcwrasjylewyivtmu
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