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1. Part Aware Contrastive Learning for Self-Supervised Action Recognition

   Yilei Hua, Wenhan Wu (October 2023, THE 32nd INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE)

   In recent years, remarkable results have been achieved in self-supervised action recognition using skeleton sequences with contrastive learning. It has been observed that the semantic distinction of human action features is often represented by local body parts, such as legs or hands, which are advantageous for skeleton-based action recognition. This paper proposes an attention-based contrastive learning framework for skeleton representation learning, called SkeAttnCLR, which integrates local similarity and global features for skeleton-based action representations. To achieve this, a multi-head attention mask module is employed to learn the soft attention mask features from the skeletons, suppressing non-salient local features while accentuating local salient features, thereby bringing similar local features closer in the feature space. Additionally, ample contrastive pairs are generated by expanding contrastive pairs based on salient and non-salient features with global features, which guide the network to learn the semantic representations of the entire skeleton. Therefore, with the attention mask mechanism, SkeAttnCLR learns local features under different data augmentation views. The experiment results demonstrate that the inclusion of local feature similarity significantly enhances skeleton-based action representation. Our proposed SkeAttnCLR outperforms state-of-the-art methods on NTURGB+D, NTU120-RGB+D, and PKU-MMD datasets. The code and settings are available at this repository: https://github.com/GitHubOfHyl97/SkeAttnCLR. 

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2. Decentralized Unsupervised Learning of Visual Representations

   https://doi.org/10.24963/ijcai.2022/323  

   Wu, Yawen; Wang, Zhepeng; Zeng, Dewen; Li, Meng; Shi, Yiyu; Hu, Jingtong (July 2022, International Joint Conference on Artificial Intelligence)

   Collaborative learning enables distributed clients to learn a shared model for prediction while keeping the training data local on each client. However, existing collaborative learning methods require fully-labeled data for training, which is inconvenient or sometimes infeasible to obtain due to the high labeling cost and the requirement of expertise. The lack of labels makes collaborative learning impractical in many realistic settings. Self-supervised learning can address this challenge by learning from unlabeled data. Contrastive learning (CL), a self-supervised learning approach, can effectively learn visual representations from unlabeled image data. However, the distributed data collected on clients are usually not independent and identically distributed (non-IID) among clients, and each client may only have few classes of data, which degrades the performance of CL and learned representations. To tackle this problem, we propose a collaborative contrastive learning framework consisting of two approaches: feature fusion and neighborhood matching, by which a unified feature space among clients is learned for better data representations. Feature fusion provides remote features as accurate contrastive information to each client for better local learning. Neighborhood matching further aligns each client’s local features to the remote features such that well-clustered features among clients can be learned. Extensive experiments show the effectiveness of the proposed framework. It outperforms other methods by 11% on IID data and matches the performance of centralized learning. 

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3. EDLT: Enabling Deep Learning for Generic Data Classification

   https://doi.org/10.1109/ICDM.2018.00030  

   Han, Huimei; Zhu, Xingquan; Li, Ying (November 2018, Proc. of the 18th IEEE International Conference on Data Mining (ICDM- 2018))

   This paper proposes to enable deep learning for generic machine learning tasks. Our goal is to allow deep learning to be applied to data which are already represented in instance feature tabular format for a better classification accuracy. Because deep learning relies on spatial/temporal correlation to learn new feature representation, our theme is to convert each instance of the original dataset into a synthetic matrix format to take the full advantage of the feature learning power of deep learning methods. To maximize the correlation of the matrix , we use 0/1 optimization to reorder features such that the ones with strong correlations are adjacent to each other. By using a two dimensional feature reordering, we are able to create a synthetic matrix, as an image, to represent each instance. Because the synthetic image preserves the original feature values and data correlation, existing deep learning algorithms, such as convolutional neural networks (CNN), can be applied to learn effective features for classification. Our experiments on 20 generic datasets, using CNN as the deep learning classifier, confirm that enabling deep learning to generic datasets has clear performance gain, compared to generic machine learning methods. In addition, the proposed method consistently outperforms simple baselines of using CNN for generic dataset. As a result, our research allows deep learning to be broadly applied to generic datasets for learning and classification 

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4. Learning Convolutional Neural Networks from Ordered Features of Generic Data

   https://doi.org/10.1109/ICMLA.2018.00145  

   Golinko, Eric; Sonderman, Thomas; Zhu, Xingquan (December 2018, The 17th IEEE International Conference on Machine Learning and Applications (ICMLA))

   Convolutional neural networks (CNN) have become very popular for computer vision, text, and sequence tasks. CNNs have the advantage of being able to learn local patterns through convolution filters. However, generic datasets do not have meaningful local data correlations, because their features are assumed to be independent of each other. In this paper, we propose an approach to reorder features of a generic dataset to create feature correlations for CNN to learn feature representation, and use learned features as inputs to help improve traditional machine learning classifiers. Our experiments on benchmark data exhibit increased performance and illustrate the benefits of using CNNs for generic datasets. 

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5. CSI: Contrastive data Stratification for Interaction prediction and its application to compound–protein interaction prediction

   https://doi.org/10.1093/bioinformatics/btad456  

   Kalia, Apurva; Krishnan, Dilip; Hassoun, Soha (August 2023, Bioinformatics)

   Martelli, Pier Luigi (Ed.)

   Motivation: Accurately predicting the likelihood of interaction between two objects (compound–protein sequence, user–item, author–paper, etc.) is a fundamental problem in Computer Science. Current deep-learning models rely on learning accurate representations of the interacting objects. Importantly, relationships between the interacting objects, or features of the interaction, offer an opportunity to partition the data to create multi-views of the interacting objects. The resulting congruent and non-congruent views can then be exploited via contrastive learning techniques to learn enhanced representations of the objects. Results: We present a novel method, Contrastive Stratification for Interaction Prediction (CSI), to stratify (partition) a dataset in a manner that can be exploited via Contrastive Multiview Coding to learn embeddings that maximize the mutual information across congruent data views. CSI assigns a key and multiple views to each data point, where data partitions under a particular key form congruent views of the data. We showcase the effectiveness of CSI by applying it to the compound–protein sequence interaction prediction problem, a pressing problem whose solution promises to expedite drug delivery (drug–protein interaction prediction), metabolic engineering, and synthetic biology (compound–enzyme interaction prediction) applications. Comparing CSI with a baseline model that does not utilize data stratification and contrastive learning, and show gains in average precision ranging from 13.7% to 39% using compounds and sequences as keys across multiple drug–target and enzymatic datasets, and gains ranging from 16.9% to 63% using reaction features as keys across enzymatic datasets. 

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