{"id":"W2396976214","doi":"","title":"Using very deep autoencoders for content-based image retrieval.","year":2011,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":375,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Image retrieval; Binary code; Hash function; Pattern recognition (psychology); Content-based image retrieval; Set (abstract data type); Binary number; Image (mathematics); Matching (statistics); Deep learning; Binary image; Computer vision; Image processing; Mathematics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007810542,0.0009357054,0.0006925127,0.0006432888,0.0002449796,0.0007144119,0.001057795,0.001209602,0.002643695],"category_scores_gemma":[0.002018616,0.0004930922,0.0005644339,0.0006265506,0.0004736037,0.001684803,0.0006488471,0.001312137,0.001496972],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008905967,"about_ca_system_score_gemma":0.0004631076,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00570955,"about_ca_topic_score_gemma":0.009019022,"domain_scores_codex":[0.9997064,0.00007331689,0.00002090042,0.00006595869,0.00009088134,0.00004247896],"domain_scores_gemma":[0.9994005,0.0002619054,0.00005168583,0.0001080754,0.0001531941,0.00002462401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003168291,0.0002801205,0.001117819,0.0002284062,0.0002350424,0.000137594,0.00007951266,0.2999936,0.04113448,0.006776709,0.01116171,0.6385381],"study_design_scores_gemma":[0.000009284294,0.00003286527,0.0002183858,0.000007360742,0.00001387511,0.00002907772,0.00000619765,0.9900614,0.00612165,0.002748214,0.0007441796,0.000007539983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02987312,0.001698593,0.9624122,0.0003076463,0.00018949,0.0000737579,0.0002190058,0.002974941,0.002251239],"genre_scores_gemma":[0.5023547,0.001079004,0.4856016,0.0005180995,0.0001905575,0.0001586581,0.001071462,0.0002156072,0.008810299],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00570955,"threshold_uncertainty_score":0.01135266,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1788960244118397,"score_gpt":0.3284254669151485,"score_spread":0.1495294425033087,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}