Vondrick, CarlPirsiavash, HamedOliva, AudeTorralba, Antonio2019-07-032019-07-032015Carl Vondrick, et.al, Learning visual biases from human imagination, Advances in Neural Information Processing Systems 28 (NIPS 2015), http://papers.nips.cc/paper/5781-learning-visual-biases-from-human-imaginationhttp://hdl.handle.net/11603/14338Advances in Neural Information Processing Systems 28 (NIPS 2015).Although the human visual system can recognize many concepts under challengingconditions, it still has some biases. In this paper, we investigate whether wecan extract these biases and transfer them into a machine recognition system.We introduce a novel method that, inspired by well-known tools in humanpsychophysics, estimates the biases that the human visual system might use forrecognition, but in computer vision feature spaces. Our experiments aresurprising, and suggest that classifiers from the human visual system can betransferred into a machine with some success. Since these classifiers seem tocapture favorable biases in the human visual system, we further present an SVMformulation that constrains the orientation of the SVM hyperplane to agree withthe bias from human visual system. Our results suggest that transferring thishuman bias into machines may help object recognition systems generalize acrossdatasets and perform better when very little training data is available.9 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.human imaginationvisual biasesSVMcomputer visionText