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{
    "pk": 25840,
    "title": "Efficient analysis-by-synthesis in vision: A computational framework, behavioral\ntests, and comparison with neural representations",
    "subtitle": null,
    "abstract": "A glance at an object is often sufficient to recognize it and\nrecover fine details of its shape and appearance, even under\nhighly variable viewpoint and lighting conditions. How can\nvision be so rich, but at the same time fast? The analysisby-\nsynthesis approach to vision offers an account of the richness\nof our percepts, but it is typically considered too slow\nto explain perception in the brain. Here we propose a version\nof analysis-by-synthesis in the spirit of the Helmholtz machine\n(Dayan, Hinton, Neal, & Zemel, 1995) that can be implemented\nefficiently, by combining a generative model based\non a realistic 3D computer graphics engine with a recognition\nmodel based on a deep convolutional network. The recognition\nmodel initializes inference in the generative model, which\nis then refined by brief runs of MCMC. We test this approach\nin the domain of face recognition and show that it meets several\nchallenging desiderata: it can reconstruct the approximate\nshape and texture of a novel face from a single view, at a level\nindistinguishable to humans; it accounts quantitatively for human\nbehavior in “hard” recognition tasks that foil conventional\nmachine systems; and it qualitatively matches neural responses\nin a network of face-selective brain areas. Comparison to other\nmodels provides insights to the success of our model.",
    "language": "eng",
    "license": {
        "name": "",
        "short_name": "",
        "text": null,
        "url": ""
    },
    "keywords": [
        {
            "word": "analysis-by-synthesis"
        },
        {
            "word": "3d scene understanding"
        },
        {
            "word": "face processing"
        },
        {
            "word": "neural"
        },
        {
            "word": "Behavioral"
        }
    ],
    "section": "Papers",
    "is_remote": true,
    "remote_url": "https://escholarship.org/uc/item/10j5s56s",
    "frozenauthors": [
        {
            "first_name": "Ilker",
            "middle_name": "",
            "last_name": "Yildirim",
            "name_suffix": "",
            "institution": "MIT",
            "department": ""
        },
        {
            "first_name": "Tejas",
            "middle_name": "D",
            "last_name": "Kulkarni",
            "name_suffix": "",
            "institution": "MIT",
            "department": ""
        },
        {
            "first_name": "Winrich",
            "middle_name": "A",
            "last_name": "Freiwald",
            "name_suffix": "",
            "institution": "Rockefeller University",
            "department": ""
        },
        {
            "first_name": "Joshua",
            "middle_name": "B",
            "last_name": "Tenenbaum",
            "name_suffix": "",
            "institution": "MIT",
            "department": ""
        }
    ],
    "date_submitted": null,
    "date_accepted": null,
    "date_published": "2015-01-01T18:00:00Z",
    "render_galley": null,
    "galleys": [
        {
            "label": "PDF",
            "type": "pdf",
            "path": "https://journalpub.escholarship.org/cognitivesciencesociety/article/25840/galley/15464/download/"
        }
    ]
}