2023 - Research.com Rising Star of Science Award
2022 - Research.com Rising Star of Science Award
Her main research concerns Artificial intelligence, Machine learning, Adversarial system, Pattern recognition and Adaptation. Many of her research projects under Artificial intelligence are closely connected to Transfer with Transfer, tying the diverse disciplines of science together. Her Segmentation research is multidisciplinary, relying on both Adversarial process, Pixel and Image.
Her Feature research is multidisciplinary, incorporating elements of Range, Variety, Concept learning and Visual recognition. Her study in Discriminative model is interdisciplinary in nature, drawing from both Object, Unsupervised learning and Contextual image classification. The Pattern recognition study combines topics in areas such as Data mining and Model selection.
Her primary areas of study are Artificial intelligence, Machine learning, Pattern recognition, Adaptation and Image. Her research brings together the fields of Computer vision and Artificial intelligence. Her biological study spans a wide range of topics, including Adversarial system and Cognitive neuroscience of visual object recognition.
While the research belongs to areas of Cognitive neuroscience of visual object recognition, Judy Hoffman spends her time largely on the problem of Variety, intersecting her research to questions surrounding Visual recognition, Range, Concept learning and Feature. The Classifier research Judy Hoffman does as part of her general Pattern recognition study is frequently linked to other disciplines of science, such as Transfer, therefore creating a link between diverse domains of science. Her Image research focuses on Variation and how it relates to Theoretical computer science and Value.
Judy Hoffman mainly focuses on Artificial intelligence, Machine learning, Embodied cognition, Generalization and Baseline. Her Artificial intelligence study typically links adjacent topics like Pattern recognition. Her work deals with themes such as Adversarial system, Entropy, Entropy and Robustness, which intersect with Machine learning.
Her Robustness study incorporates themes from Artificial neural network, Decision boundary, Discriminative model and Convolutional neural network. The concepts of her Embodied cognition study are interwoven with issues in Robot and Human–computer interaction. Her study looks at the relationship between Human–computer interaction and fields such as Leverage, as well as how they intersect with chemical problems.
Judy Hoffman mostly deals with Artificial intelligence, Baseline, Embodied cognition, Visual perception and Human–computer interaction. Test set, Robustness, Adversarial system, Artificial neural network and Decision boundary are among the areas of Artificial intelligence where she concentrates her study. Transfer, Code, Decoupling, Visualization and Robot are fields of study that overlap with her Baseline research.
While working on this project, Judy Hoffman studies both Embodied cognition and Task analysis.
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Jeff Donahue;Yangqing Jia;Oriol Vinyals;Judy Hoffman.
international conference on machine learning (2014)
Adversarial Discriminative Domain Adaptation
Eric Tzeng;Judy Hoffman;Kate Saenko;Trevor Darrell.
computer vision and pattern recognition (2017)
Deep Domain Confusion: Maximizing for Domain Invariance
Eric Tzeng;Judy Hoffman;Ning Zhang;Kate Saenko.
computer vision and pattern recognition (2014)
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Judy Hoffman;Eric Tzeng;Taesung Park;Jun-Yan Zhu.
international conference on machine learning (2018)
Simultaneous Deep Transfer Across Domains and Tasks
Eric Tzeng;Judy Hoffman;Trevor Darrell;Kate Saenko.
international conference on computer vision (2015)
FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
Judy Hoffman;Dequan Wang;Fisher Yu;Trevor Darrell.
arXiv: Computer Vision and Pattern Recognition (2016)
Inferring and Executing Programs for Visual Reasoning
Justin Johnson;Bharath Hariharan;Laurens van der Maaten;Judy Hoffman.
international conference on computer vision (2017)
VisDA: The Visual Domain Adaptation Challenge
Xingchao Peng;Ben Usman;Neela Kaushik;Judy Hoffman.
arXiv: Computer Vision and Pattern Recognition (2017)
Cross Modal Distillation for Supervision Transfer
Saurabh Gupta;Judy Hoffman;Jitendra Malik.
computer vision and pattern recognition (2016)
LSDA: Large Scale Detection through Adaptation
Judy Hoffman;Sergio Guadarrama;Eric S Tzeng;Ronghang Hu.
neural information processing systems (2014)
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