OOD-related papers

Deep One-Class Classification, ICLR 2021 [Paper] [Code]

Google AI [Blog] [Code]

Learning from Failure: Training Debiased Classifier from Biased Classifier [Paper] [Code] NeurIPS 2020

Learning Debiased Representation via Disentangled Feature Augmentation [Paper] [Code] NeurIPS 2021

Graph Convolution for Semi-Supervised Classification: Improved Linear Separability and Out-of-Distribution Generalization [Paper]

Explainable Deep One-class Classification [Paper]

GAN Ensemble for Anomaly Detection [Paper]

Neural Transformation Learning for Deep Anomaly Detection Beyond Images [Paper]

Learning Semantic Context from Normal Samples for Unsupervised Anomaly Detection

Dual Compositional Learning in Interactive Image Retrieval [*]



SSL 

Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity [Paper] [Code] NeurIPS 2021

Unsupervised Learning of Discriminative Attributes and Visual Representations [Paper] CVPR2016

Common Visual Pattern Discovery and Search [Paper]

Unsupervised Learning of Visual Representations by Solving Jigsaw Puzzles [Paper]

Discovering States and Transformations in Image Collections [Paper]

 




Comments

Post a Comment

Popular posts from this blog

Reading CLIP

Learning directly from raw text about images is promising which leverages a much broader source of supervision. Task: predicting which caption goes with which image is an efficient and scalable way to learn SOTA image representations from scratch on a dataset of pairs. After pre-training, natural language is used to is used to reference learned visual concepts enabling zero-shot transfer of the model to downstream tasks.   [Paper Source]  https://cdn.openai.com/papers/Learning_Transferable_Visual_Models_From_Natural_Language.pdf Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J. and Krueger, G., 2021. Learning transferable visual models from natural language supervision.  arXiv preprint arXiv:2103.00020 .
Read more

Reading CutPaste

 CutPaste: a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Dataset: MVTec anomaly detection dataset Aim: detect various types of real-world defects [ Code ] Paper Source:  https://openaccess.thecvf.com/content/CVPR2021/papers/Li_CutPaste_Self-Supervised_Learning_for_Anomaly_Detection_and_Localization_CVPR_2021_paper.pdf
Read more