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When it comes to AI, can we ditch the datasets?

MIT researchers have developed a technique to train a machine-learning model for image classification, which does not require the use of a dataset. Instead, they use a “generative model” to produce synthetic data that is used to train an image classifier, which can then perform as well as or better than an image classifier trained using real data. ....

United States , Ali Jahanian , Xavier Puig , Phillip Isola , International Conference On Learning Representations , Artificial Intelligence Laboratory , Watson Ai Lab , United States Air Force Artificial Intelligence Accelerator , Department Of Electrical Engineering , United States Air Force Research Laboratory , Computer Science , Yonglong Tian , Electrical Engineering , International Conference , United States Air Force Artificial Intelligence , Phillip Isola , Enerative Models Training , Contrastive Learning ,

ETRI Secures First Place in International Competition of Tracking Objects in the Field of Autonomous Driving

Technologies of object segmentation and tracking for autonomous driving jointly developed by the Korean research team and the research team of the University of Washington won the first place prize in an International competition of tracking objects in the field of autonomous driving. ....

United States , Laura Thomsondec , Telecommunications Research Institute , University Of Washington , Germany Freiburg University , International Conference On Computer Vision , International Conference , Computer Vision , Freiburg University , Multi Target Tracking , Contrastive Learning , Multi Target Tracking , Advanced Video , Signal Based Surveillance ,

"Fuzzy contrastive learning for online behavior analysis" by Jie Yang, Gang Huang et al.

With the prevalence of smart devices, billions of people are accessing digital resource in their daily life. Online user-behavior modeling, as such, has been actively researched in recent years. However, due to the data uncertainty (sparse-ness and skewness), traditional techniques suffer from certain drawbacks, such as relying on labor-intensive expertise or prior knowledge, lacking of interpretability and transparency, and expensive computational cost. As a step toward bridging the gap, this paper proposes a fuzzy-set based contrastive learning algorithm. The general idea is to design an end-to-end learning framework of optimizing representation from contrastive samples. The proposed algorithm is characterized by three main modules, including data augmentation, fuzzy encoder, and semi-supervised optimization. More precisely, data augmentation is used to produce contrastive (positive and negative) samples based on anchor ones. The fuzzy encoder is introduced to fuzzify (or encode) lat ....

Contrastive Learning , Fuzzy Set , Obile Applications Analysis , Self Supervised Learning , Ser Online Behavior ,

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