{"id":"W2798419898","doi":"10.1109/cvprw.2018.00032","title":"Semantic Binary Segmentation Using Convolutional Networks without Decoders","year":2018,"lang":"en","type":"preprint","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Natural Sciences and Engineering Research Council of Canada; Canada First Research Excellence Fund; Nvidia","keywords":"Segmentation; Computer science; Encoder; Artificial intelligence; Convolutional neural network; Computation; Pattern recognition (psychology); Scale-space segmentation; Binary number; Image segmentation; Encoding (memory); Feature (linguistics); Computer vision; Algorithm; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001671693,0.0003016162,0.0002543243,0.0001268505,0.0002871886,0.000170285,0.001046394,0.0002129315,0.000043348],"category_scores_gemma":[0.000009397303,0.0003061984,0.0001032997,0.0003430349,0.0001521029,0.0003613055,0.001712151,0.0003980226,0.00005999392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002112768,"about_ca_system_score_gemma":0.0001715513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003118519,"about_ca_topic_score_gemma":0.00001572221,"domain_scores_codex":[0.9979423,0.00008187893,0.000388983,0.0008887628,0.0003156076,0.0003824253],"domain_scores_gemma":[0.998315,0.00009404084,0.0003199801,0.0009657799,0.0001830212,0.000122206],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000006786824,0.00005137292,0.002010744,0.00002731413,0.00005073708,0.000004226925,0.00006987515,0.9821122,0.0004994978,0.009716138,0.002832045,0.002619052],"study_design_scores_gemma":[0.0001433062,0.0000190718,0.0009385154,0.00006070776,0.00002175815,0.00002119662,0.000007020473,0.9831236,0.0001440504,0.01501771,0.000168121,0.0003349181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.019944,0.0001844377,0.9768763,0.000397655,0.001032151,0.0006275843,0.00000349806,0.000440563,0.0004938453],"genre_scores_gemma":[0.4118971,0.00006141865,0.5866598,0.0005352415,0.0004503345,0.00008351298,0.0000662609,0.000026222,0.0002201585],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3919531,"threshold_uncertainty_score":0.999939,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04271934472222172,"score_gpt":0.3196910141322704,"score_spread":0.2769716694100487,"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."}}