C3: Cross-instance guided Contrastive Clustering

Clustering is the task of gathering similar data samples into clusters without using any predefined labels. It has been widely studied in machine learning literature, and recent advancements in deep learning have revived interest in this field. Contrastive clustering (CC) models are a staple of deep clustering in which positive and negative pairs of each data instance are generated through data augmentation. CC models aim to learn a feature space where instance-level and cluster-level representations of positive pairs are grouped together. Despite improving the SOTA, these algorithms ignore the cross-instance patterns, which carry essential information for improving clustering performance. This increases the false-negative-pair rate of the model while decreasing its true-positive-pair rate. In this paper, we propose a novel contrastive clustering method, Cross-instance guided Contrastive Clustering (C3), that considers the cross-sample relationships to increase the number of positive pairs and mitigate the impact of false negative, noise, and anomaly sample on the learned representation of data. In particular, we define a new loss function that identifies similar instances using the instance-level representation and encourages them to aggregate together. Moreover, we propose a novel weighting method to select negative samples in a more efficient way. Extensive experimental evaluations show that our proposed method can outperform state-of-the-art algorithms on benchmark computer vision datasets: we improve the clustering accuracy by 6.6%, 3.3%, 5.0%, 1.3% and 0.3% on CIFAR-10, CIFAR-100, ImageNet-10, ImageNet-Dogs, and Tiny-ImageNet.Comment: 10 pages, 8 Figures, 1 Table

Contrastive Clustering

In this paper, we propose a one-stage online clustering method called Contrastive Clustering (CC) which explicitly performs the instance- and cluster-level contrastive learning. To be specific, for a given dataset, the positive and negative instance pairs are constructed through data augmentations and then projected into a feature space. Therein, the instance- and cluster-level contrastive learning are respectively conducted in the row and column space by maximizing the similarities of positive pairs while minimizing those of negative ones. Our key observation is that the rows of the feature matrix could be regarded as soft labels of instances, and accordingly the columns could be further regarded as cluster representations. By simultaneously optimizing the instance- and cluster-level contrastive loss, the model jointly learns representations and cluster assignments in an end-to-end manner. Extensive experimental results show that CC remarkably outperforms 17 competitive clustering methods on six challenging image benchmarks. In particular, CC achieves an NMI of 0.705 (0.431) on the CIFAR-10 (CIFAR-100) dataset, which is an up to 19\% (39\%) performance improvement compared with the best baseline

Information Maximization Clustering via Multi-View Self-Labelling

Image clustering is a particularly challenging computer vision task, which aims to generate annotations without human supervision. Recent advances focus on the use of self-supervised learning strategies in image clustering, by first learning valuable semantics and then clustering the image representations. These multiple-phase algorithms, however, increase the computational time and their final performance is reliant on the first stage. By extending the self-supervised approach, we propose a novel single-phase clustering method that simultaneously learns meaningful representations and assigns the corresponding annotations. This is achieved by integrating a discrete representation into the self-supervised paradigm through a classifier net. Specifically, the proposed clustering objective employs mutual information, and maximizes the dependency between the integrated discrete representation and a discrete probability distribution. The discrete probability distribution is derived though the self-supervised process by comparing the learnt latent representation with a set of trainable prototypes. To enhance the learning performance of the classifier, we jointly apply the mutual information across multi-crop views. Our empirical results show that the proposed framework outperforms state-of-the-art techniques with the average accuracy of 89.1% and 49.0%, respectively, on CIFAR-10 and CIFAR-100/20 datasets. Finally, the proposed method also demonstrates attractive robustness to parameter settings, making it ready to be applicable to other datasets

Temporal Action Segmentation: An Analysis of Modern Techniques

Temporal action segmentation (TAS) in videos aims at densely identifying video frames in minutes-long videos with multiple action classes. As a long-range video understanding task, researchers have developed an extended collection of methods and examined their performance using various benchmarks. Despite the rapid growth of TAS techniques in recent years, no systematic survey has been conducted in these sectors. This survey analyzes and summarizes the most significant contributions and trends. In particular, we first examine the task definition, common benchmarks, types of supervision, and prevalent evaluation measures. In addition, we systematically investigate two essential techniques of this topic, i.e., frame representation and temporal modeling, which have been studied extensively in the literature. We then conduct a thorough review of existing TAS works categorized by their levels of supervision and conclude our survey by identifying and emphasizing several research gaps. In addition, we have curated a list of TAS resources, which is available at https://github.com/nus-cvml/awesome-temporal-action-segmentation.Comment: 19 pages, 9 figures, 8 table

Deep Clustering: A Comprehensive Survey

Cluster analysis plays an indispensable role in machine learning and data mining. Learning a good data representation is crucial for clustering algorithms. Recently, deep clustering, which can learn clustering-friendly representations using deep neural networks, has been broadly applied in a wide range of clustering tasks. Existing surveys for deep clustering mainly focus on the single-view fields and the network architectures, ignoring the complex application scenarios of clustering. To address this issue, in this paper we provide a comprehensive survey for deep clustering in views of data sources. With different data sources and initial conditions, we systematically distinguish the clustering methods in terms of methodology, prior knowledge, and architecture. Concretely, deep clustering methods are introduced according to four categories, i.e., traditional single-view deep clustering, semi-supervised deep clustering, deep multi-view clustering, and deep transfer clustering. Finally, we discuss the open challenges and potential future opportunities in different fields of deep clustering

동종, 이종, 그리고 나무 형태의 그래프를 위한 비지도 표현 학습

학위논문(박사) -- 서울대학교대학원 : 공과대학 전기·정보공학부, 2022. 8. 최진영.그래프 데이터에 대한 비지도 표현 학습의 목적은 그래프의 구조와 노드의 속성을 잘 반영하는 유용한 노드 단위 혹은 그래프 단위의 벡터 형태 표현을 학습하는 것이다. 최근, 그래프 데이터에 대해 강력한 표현 학습 능력을 갖춘 그래프 신경망을 활용한 비지도 그래프 표현 학습 모델의 설계가 주목을 받고 있다. 많은 방법들은 한 종류의 엣지와 한 종류의 노드가 존재하는 동종 그래프에 대한 학습에 집중을 한다. 하지만 이 세상에 수많은 종류의 관계가 존재하기 때문에, 그래프 또한 구조적, 의미론적 속성을 통해 다양한 종류로 분류할 수 있다. 그래서, 그래프로부터 유용한 표현을 학습하기 위해서는 비지도 학습 프레임워크는 입력 그래프의 특징을 제대로 고려해야만 한다. 본 학위논문에서 우리는 널리 접할 수 있는 세가지 그래프 구조인 동종 그래프, 트리 형태의 그래프, 그리고 이종 그래프에 대한 그래프 신경망을 활용하는 비지도 학습 모델들을 제안한다. 처음으로, 우리는 동종 그래프의 노드에 대하여 저차원 표현을 학습하는 그래프 컨볼루션 오토인코더 모델을 제안한다. 기존의 그래프 오토인코더는 구조의 전체가 학습이 불가능해서 제한적인 표현 학습 능력을 가질 수 있는 반면에, 제안하는 오토인코더는 노드의 피쳐를 복원하며,구조의 전체가 학습이 가능하다. 노드의 피쳐를 복원하기 위해서, 우리는 인코더 부분의 역할이 이웃한 노드끼리 유사한 표현을 가지게 하는 라플라시안 스무딩이라는 것에 주목하여 디코더 부분에서는 이웃 노드의 표현과 멀어지게 하는 라플라시안 샤프닝을 하도록 설계하였다. 또한 라플라시안 샤프닝을 그대로 적용하면 불안정성을 유발할 수 있기 때문에, 엣지의 가중치 값에 음의 값을 줄 수 있는 부호형 그래프를 활용하여 안정적인 라플라시안 샤프닝의 형태를 제안하였다. 동종 그래프에 대한 노드 클러스터링과 링크 예측 실험을 통하여 제안하는 방법이 안정적으로 우수한 성능을 보임을 확인하였다. 둘째로, 우리는 트리의 형태를 가지는 계층적인 관계를 가지고 있는 그래프의 노드 표현을 정확하게 학습하기 위하여 쌍곡선 공간에서 동작하는 오토인코더 모델을 제안한다. 유클리디언 공간은 트리를 사상하기에 부적절하다는 최근의 분석을 통하여, 쌍곡선 공간에서 그래프 신경망의 레이어를 활용하여 노드의 저차원 표현을 학습하게 된다. 이 때, 그래프 신경망이 쌍곡선 기하학에서 계층 정보를 담고 있는 거리의 값을 활용하여 노드의 이웃사이의 중요도를 활용하도록 설계하였다. 우리는 논문 인용 관계 네트워크, 계통도, 이미지 사이의 네트워크등에 대해 제안한 모델을 적용하여 노드 클러스터링과 링크 예측 실험을 하였으며, 트리의 형태를 가지는 그래프에 대해서 제안한 모델이 유클리디언 공간에서 수행하는 모델에 비해 향상된 성능을 보였다는 것을 확인하였다. 마지막으로, 우리는 여러 종류의 노드와 엣지를 가지는 이종그래프에 대한 대조 학습 모델을 제안한다. 우리는 기존의 방법들이 학습하기 이전에 충분한 도메인 지식을 사용하여 설계한 메타패스나 메타그래프에 의존한다는 단점과 많은 이종그래프의 엣지가 다른 노드 종류사이의 관계에 집중하고 있다는 점을 주목하였다. 이를 통해 우리는 사전과정이 필요없으며 다른 종류 사이의 관계에 더하여 같은 종류 사이의 관계도 동시에 효율적으로 학습하게 하는 메타노드라는 개념을 제안하였다. 또한 메타노드를 기반으로하는 그래프 신경망과 대조 학습 모델을 제안하였다. 우리는 제안한 모델을 메타패스를 사용하는 이종그래프 학습 모델과 노드 클러스터링 등의 실험 성능으로 비교해보았을 때, 비등하거나 높은 성능을 보였음을 확인하였다.The goal of unsupervised graph representation learning is extracting useful node-wise or graph-wise vector representation that is aware of the intrinsic structures of the graph and its attributes. These days, designing methodology of unsupervised graph representation learning based on graph neural networks has growing attention due to their powerful representation ability. Many methods are focused on a homogeneous graph that is a network with a single type of node and a single type of edge. However, as many types of relationships exist in this world, graphs can also be classified into various types by structural and semantic properties. For this reason, to learn useful representations from graphs, the unsupervised learning framework must consider the characteristics of the input graph. In this dissertation, we focus on designing unsupervised learning models using graph neural networks for three graph structures that are widely available: homogeneous graphs, tree-like graphs, and heterogeneous graphs. First, we propose a symmetric graph convolutional autoencoder which produces a low-dimensional latent representation from a homogeneous graph. In contrast to the existing graph autoencoders with asymmetric decoder parts, the proposed autoencoder has a newly designed decoder which builds a completely symmetric autoencoder form. For the reconstruction of node features, the decoder is designed based on Laplacian sharpening as the counterpart of Laplacian smoothing of the encoder, which allows utilizing the graph structure in the whole processes of the proposed autoencoder architecture. In order to prevent the numerical instability of the network caused by the Laplacian sharpening introduction, we further propose a new numerically stable form of the Laplacian sharpening by incorporating the signed graphs. The experimental results of clustering, link prediction and visualization tasks on homogeneous graphs strongly support that the proposed model is stable and outperforms various state-of-the-art algorithms. Second, we analyze how unsupervised tasks can benefit from learned representations in hyperbolic space. To explore how well the hierarchical structure of unlabeled data can be represented in hyperbolic spaces, we design a novel hyperbolic message passing autoencoder whose overall auto-encoding is performed in hyperbolic space. The proposed model conducts auto-encoding the networks via fully utilizing hyperbolic geometry in message passing. Through extensive quantitative and qualitative analyses, we validate the properties and benefits of the unsupervised hyperbolic representations of tree-like graphs. Third, we propose the novel concept of metanode for message passing to learn both heterogeneous and homogeneous relationships between any two nodes without meta-paths and meta-graphs. Unlike conventional methods, metanodes do not require a predetermined step to manipulate the given relations between different types to enrich relational information. Going one step further, we propose a metanode-based message passing layer and a contrastive learning model using the proposed layer. In our experiments, we show the competitive performance of the proposed metanode-based message passing method on node clustering and node classification tasks, when compared to state-of-the-art methods for message passing networks for heterogeneous graphs.1 Introduction 1 2 Representation Learning on Graph-Structured Data 4 2.1 Basic Introduction 4 2.1.1 Notations 5 2.2 Traditional Approaches 5 2.2.1 Graph Statistic 5 2.2.2 Neighborhood Overlap 7 2.2.3 Graph Kernel 9 2.2.4 Spectral Approaches 10 2.3 Node Embeddings I: Factorization and Random Walks 15 2.3.1 Factorization-based Methods 15 2.3.2 Random Walk-based Methods 16 2.4 Node Embeddings II: Graph Neural Networks 17 2.4.1 Overview of Framework 17 2.4.2 Representative Models 18 2.5 Learning in Unsupervised Environments 21 2.5.1 Predictive Coding 21 2.5.2 Contrastive Coding 22 2.6 Applications 24 2.6.1 Classifications 24 2.6.2 Link Prediction 26 3 Autoencoder Architecture for Homogeneous Graphs 27 3.1 Overview 27 3.2 Preliminaries 30 3.2.1 Spectral Convolution on Graphs 30 3.2.2 Laplacian Smoothing 32 3.3 Methodology 33 3.3.1 Laplacian Sharpening 33 3.3.2 Numerically Stable Laplacian Sharpening 34 3.3.3 Subspace Clustering Cost for Image Clustering 37 3.3.4 Training 39 3.4 Experiments 40 3.4.1 Datasets 40 3.4.2 Experimental Settings 42 3.4.3 Comparing Methods 42 3.4.4 Node Clustering 43 3.4.5 Image Clustering 45 3.4.6 Ablation Studies 46 3.4.7 Link Prediction 47 3.4.8 Visualization 47 3.5 Summary 49 4 Autoencoder Architecture for Tree-like Graphs 50 4.1 Overview 50 4.2 Preliminaries 52 4.2.1 Hyperbolic Embeddings 52 4.2.2 Hyperbolic Geometry 53 4.3 Methodology 55 4.3.1 Geometry-Aware Message Passing 56 4.3.2 Nonlinear Activation 57 4.3.3 Loss Function 58 4.4 Experiments 58 4.4.1 Datasets 59 4.4.2 Compared Methods 61 4.4.3 Experimental Details 62 4.4.4 Node Clustering and Link Prediction 64 4.4.5 Image Clustering 66 4.4.6 Structure-Aware Unsupervised Embeddings 68 4.4.7 Hyperbolic Distance to Filter Training Samples 71 4.4.8 Ablation Studies 74 4.5 Further Discussions 75 4.5.1 Connection to Contrastive Learning 75 4.5.2 Failure Cases of Hyperbolic Embedding Spaces 75 4.6 Summary 77 5 Contrastive Learning for Heterogeneous Graphs 78 5.1 Overview 78 5.2 Preliminaries 82 5.2.1 Meta-path 82 5.2.2 Representation Learning on Heterogeneous Graphs 82 5.2.3 Contrastive methods for Heterogeneous Graphs 83 5.3 Methodology 84 5.3.1 Definitions 84 5.3.2 Metanode-based Message Passing Layer 86 5.3.3 Contrastive Learning Framework 88 5.4 Experiments 89 5.4.1 Experimental Details 90 5.4.2 Node Classification 94 5.4.3 Node Clustering 96 5.4.4 Visualization 96 5.4.5 Effectiveness of Metanodes 97 5.5 Summary 99 6 Conclusions 101박