{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/cogni-net-cognitive-feature-learning-through","title":"Cogni-Net: Cognitive Feature Learning through Deep Visual Perception","arxiv_id":"1811.00201","date":"2018-11-01","proceeding":null,"authors":["Pranay Mukherjee","Abhirup Das","Ayan Kumar Bhunia","Partha Pratim Roy"],"abstract":"Can we ask computers to recognize what we see from brain signals alone? Our\npaper seeks to utilize the knowledge learnt in the visual domain by popular\npre-trained vision models and use it to teach a recurrent model being trained\non brain signals to learn a discriminative manifold of the human brain's\ncognition of different visual object categories in response to perceived visual\ncues. For this we make use of brain EEG signals triggered from visual stimuli\nlike images and leverage the natural synchronization between images and their\ncorresponding brain signals to learn a novel representation of the cognitive\nfeature space. The concept of knowledge distillation has been used here for\ntraining the deep cognition model, CogniNet\\footnote{The source code of the\nproposed system is publicly available at\n{https://www.github.com/53X/CogniNET}}, by employing a student-teacher learning\ntechnique in order to bridge the process of inter-modal knowledge transfer. The\nproposed novel architecture obtains state-of-the-art results, significantly\nsurpassing other existing models. The experiments performed by us also suggest\nthat if visual stimuli information like brain EEG signals can be gathered on a\nlarge scale, then that would help to obtain a better understanding of the\nlargely unexplored domain of human brain cognition.","url_abs":"http://arxiv.org/abs/1811.00201v2","url_pdf":"http://arxiv.org/pdf/1811.00201v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"cogni-net-cognitive-feature-learning-through","repo_url":"https://github.com/53X/CogniNET","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"ok","spdx":"GPL-3.0"}}],"tasks":[{"task_slug":"eeg-1","task_name":"EEG"},{"task_slug":"eeg","task_name":"Electroencephalogram (EEG)"},{"task_slug":"knowledge-distillation","task_name":"Knowledge Distillation"},{"task_slug":"transfer-learning","task_name":"Transfer Learning"}],"methods":[{"method_slug":"knowledge-distillation","method_name":"Knowledge Distillation"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}