| Application |
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Representation Learning with very Limited Images: The Potential of Self-, Synthetic- and Formula-Supervision
| Title | Repo | Paper | Video |
|---|---|---|---|
| Image Guided Inpainting with Parameter Efficient Learning | ➖ |  |
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| Augmenting Features via Contrastive Learning-based Generative Model for Long-Tailed Classification | ➖ | |
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| G2L: A High-Dimensional Geometric Approach for Automatic Generation of Highly Accurate Pseudo-Labels |  |
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| Self-Supervised Hypergraphs for Learning Multiple World Interpretations | ➖ |  |
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| Deep Generative Networks for Heterogeneous Augmentation of Cranial Defects | ➖ |  |
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| 360° from a Single Camera: A Few-Shot Approach for LiDAR Segmentation | ➖ | |
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| Adaptive Self-Training for Object Detection |  |
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| FedLID: Self-Supervised Federated Learning for Leveraging Limited Image Data | ➖ | |
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| A Horse with no Labels: Self-Supervised Horse Pose Estimation from Unlabelled Images and Synthetic Prior | ➖ |  |
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| Boosting Semi-Supervised Learning by Bridging High and Low-Confidence Predictions | ➖ |  |
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| SelectNAdapt: Support Set Selection for Few-Shot Domain Adaptation |  |
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| MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection |  |
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| Enhancing Classification Accuracy on Limited Data via Unconditional GAN | ➖ | |
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| Self-Training and Multi-Task Learning for Limited Data: Evaluation Study on Object Detection | |
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| JEDI: Joint Expert Distillation in a Semi-Supervised Multi-Dataset Student-Teacher Scenario for Video Action Recognition | ➖ |  |
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| Semantic RGB-D Image Synthesis | ➖ |  |
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| Learning Universal Semantic Correspondences with No Supervision and Automatic Data Curation | ➖ | |
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| Guiding Video Prediction with Explicit Procedural Knowledge | ➖ | |
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| Frequency-Aware Self-Supervised Long-Tailed Learning | ➖ |  |
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| Tensor Factorization for Leveraging Cross-Modal Knowledge in Data-Constrained Infrared Object Detection | ➖ |  |
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