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GitHub - continuousml/Awesome-Out-Of-Distribution-Detection: A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization

A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization - GitHub - continuousml/Awesome-Out-Of-Distribution-Detection: A professionally curated list of papers, tutorials, books, videos, articles and open-source libraries etc for Out-of-distribution detection, robustness, and generalization ....

Sri Lanka , Dustin Tran , Thomasg Dietterich , Alexander Meinke , Yibo Zhou , Jie Ren , Vaishnavh Nagarajan , Balaji Lakshminarayanan , Neeraj Varshney , Sharon Yixuan Li , Jasper Snoek , Examples In Neural Networks , Presence Of Limited , Mitigating Neural Network Overconfidence , Confidence For Detection In Neural Networks , Energy Regularization Loss For Detection , Reliability Of Image Detection In Neural Networks , Detection In Deep Neural Networks , Detection Using Union , Networks Can Improve Robustness , Hopfield Energy , A Unified Survey On , Detection For Graph Neural Networks , Energy Based Model , Neural Networks , Why Relu Networks Yield High ,

Contrastive Representation Learning

The goal of contrastive representation learning is to learn such an embedding space in which similar sample pairs stay close to each other while dissimilar ones are far apart. Contrastive learning can be applied to both supervised and unsupervised settings. When working with unsupervised data, contrastive learning is one of the most powerful approaches in self-supervised learning.
Contrastive Training Objectives In early versions of loss functions for contrastive learning, only one positive and one negative sample are involved. ....

Prannay Khosla , Wang Isola , Salakhutdinov Hinton , Jason Wei , Ekind Cubuk , Lajanugen Logeswaran , Phillip Isola , Dmitry Kalenichenko , Tianyu Gao , Yazhe Li Oriol Vinyals , Joshua Robinson , Logeswaran Lee , Ching Yao Chuang , Monte Carlo , Geoffrey Hinton , Mathilde Caron , Florian Schroff , Iryna Gurevych , Geoff Hinton , Cutmix Yun , Nicolas Papernot , Aapo Hyv , Bohan Li , Dinghan Shen , Sumit Chopra , Wei Zou ,

Injecting fairness into machine-learning models

MIT researchers have found that, if a certain type of machine learning model is trained using an unbalanced dataset, the bias that it learns is impossible to fix after the fact. They developed a technique that induces fairness directly into the model, no matter how unbalanced the training dataset was, which can boost the model’s performance on downstream tasks. ....

Natalie Dullerud , Nicolas Papernot , Marzyeh Ghassemi , Karsten Roth , Kimia Hamidieh , International Conference On Learning Representations , Artificial Intelligence Laboratory , Healthy Ml Group Of The Computer Science , University Of Tubingen , University Of Toronto Department Electrical Engineering , Healthy Ml Group , Computer Science , Electrical Engineering , International Conference , Partial Attribute Decorrelation , Deep Metric Learning , Airness In Artificial Intelligence ,

Contrastive Representation Learning

Contrastive Representation Learning
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Prannay Khosla , Wang Isola , Salakhutdinov Hinton , Jason Wei , Ekind Cubuk , Lajanugen Logeswaran , Phillip Isola , Dmitry Kalenichenko , Tianyu Gao , Yazhe Li Oriol Vinyals , Joshua Robinson , Logeswaran Lee , Ching Yao Chuang , Monte Carlo , Geoffrey Hinton , Mathilde Caron , Florian Schroff , Iryna Gurevych , Geoff Hinton , Cutmix Yun , Nicolas Papernot , Aapo Hyv , Deepcluster Caron , Bohan Li , Dinghan Shen , Sumit Chopra ,