Table of Contents

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   Book Overview
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   Chapter 1 GOCA: Guided Online Cluster Assignment for Self-supervised Video Representation Learning
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   Chapter 2 Constrained Mean Shift Using Distant yet Related Neighbors for Representation Learning
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   Chapter 3 Revisiting the Critical Factors of Augmentation-Invariant Representation Learning
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   Chapter 4 CA-SSL: Class-Agnostic Semi-Supervised Learning for Detection and Segmentation
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   Chapter 5 Dual Adaptive Transformations for Weakly Supervised Point Cloud Segmentation
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   Chapter 6 Semantic-Aware Fine-Grained Correspondence
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   Chapter 7 Self-Supervised Classification Network
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   Chapter 8 Data Invariants to Understand Unsupervised Out-of-Distribution Detection
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    Chapter 9 Domain Invariant Masked Autoencoders for Self-supervised Learning from Multi-domains
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    Chapter 10 Semi-supervised Object Detection via VC Learning
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    Chapter 11 Completely Self-supervised Crowd Counting via Distribution Matching
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    Chapter 12 Coarse-To-Fine Incremental Few-Shot Learning
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    Chapter 13 Learning Unbiased Transferability for Domain Adaptation by Uncertainty Modeling
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    Chapter 14 Learn2Augment: Learning to Composite Videos for Data Augmentation in Action Recognition
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    Chapter 15 CYBORGS: Contrastively Bootstrapping Object Representations by Grounding in Segmentation
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    Chapter 16 PSS: Progressive Sample Selection for Open-World Visual Representation Learning
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    Chapter 17 Improving Self-supervised Lightweight Model Learning via Hard-Aware Metric Distillation
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    Chapter 18 Object Discovery via Contrastive Learning for Weakly Supervised Object Detection
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    Chapter 19 Stochastic Consensus: Enhancing Semi-Supervised Learning with Consistency of Stochastic Classifiers
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    Chapter 20 DiffuseMorph: Unsupervised Deformable Image Registration Using Diffusion Model
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    Chapter 21 Semi-Leak: Membership Inference Attacks Against Semi-supervised Learning
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    Chapter 22 OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning
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    Chapter 23 Embedding Contrastive Unsupervised Features to Cluster In- And Out-of-Distribution Noise in Corrupted Image Datasets
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    Chapter 24 Unsupervised Few-Shot Image Classification by Learning Features into Clustering Space
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    Chapter 25 Towards Realistic Semi-supervised Learning
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    Chapter 26 Masked Siamese Networks for Label-Efficient Learning
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    Chapter 27 Natural Synthetic Anomalies for Self-supervised Anomaly Detection and Localization
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    Chapter 28 Understanding Collapse in Non-contrastive Siamese Representation Learning
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    Chapter 29 Federated Self-supervised Learning for Video Understanding
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    Chapter 30 Towards Efficient and Effective Self-supervised Learning of Visual Representations
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    Chapter 31 DSR – A Dual Subspace Re-Projection Network for Surface Anomaly Detection
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    Chapter 32 PseudoAugment: Learning to Use Unlabeled Data for Data Augmentation in Point Clouds
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    Chapter 33 MVSTER: Epipolar Transformer for Efficient Multi-view Stereo
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    Chapter 34 RelPose: Predicting Probabilistic Relative Rotation for Single Objects in the Wild
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    Chapter 35 R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis
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    Chapter 36 KD-MVS: Knowledge Distillation Based Self-supervised Learning for Multi-view Stereo
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    Chapter 37 SALVe: Semantic Alignment Verification for Floorplan Reconstruction from Sparse Panoramas
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    Chapter 38 RC-MVSNet: Unsupervised Multi-View Stereo with Neural Rendering
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    Chapter 39 Box2Mask: Weakly Supervised 3D Semantic Instance Segmentation using Bounding Boxes
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    Chapter 40 NeILF: Neural Incident Light Field for Physically-based Material Estimation
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    Chapter 41 ARF: Artistic Radiance Fields
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    Chapter 42 Multiview Stereo with Cascaded Epipolar RAFT