{"id":"W3215957616","doi":"10.1109/tip.2021.3128311","title":"A Prototypical Knowledge Oriented Adaptation Framework for Semantic Segmentation","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada; China Scholarship Council","keywords":"Discriminative model; Segmentation; Leverage (statistics); Overfitting; Discriminator; Domain adaptation; Convolutional neural network; Feature learning; Transfer of learning; Pattern recognition (psychology)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005720687,0.0007654662,0.0006346394,0.0006779107,0.0003637527,0.0007419356,0.001416283,0.001210239,0.001715385],"category_scores_gemma":[0.001065451,0.0002844501,0.0007007131,0.0008099075,0.001165619,0.001401399,0.00157386,0.001457858,0.0008205597],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005757298,"about_ca_system_score_gemma":0.0008174478,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002038137,"about_ca_topic_score_gemma":0.002542811,"domain_scores_codex":[0.9995907,0.00007334771,0.00001467421,0.000173297,0.0001012954,0.00004670791],"domain_scores_gemma":[0.9997532,0.00006401434,0.00002868178,0.0000749705,0.00005364378,0.00002548379],"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":[0.0001633428,0.0001687314,0.000813276,0.0001733503,0.0001154237,0.0003457193,0.0002877235,0.4810217,0.04959658,0.05123152,0.006722359,0.4093603],"study_design_scores_gemma":[0.000005124666,0.00002892893,0.0002139159,0.000007705598,0.000009217231,0.0001436639,0.00001920941,0.9688414,0.00612462,0.02154491,0.003048294,0.00001291309],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006025312,0.0002349971,0.9912276,0.0001215722,0.00003709043,0.0000280739,0.00006167808,0.0007718306,0.001491861],"genre_scores_gemma":[0.5169759,0.0008202079,0.4726446,0.0006049325,0.0001647741,0.000173024,0.0007973412,0.0003866323,0.007432566],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002038137,"threshold_uncertainty_score":0.005738556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03283645738904421,"score_gpt":0.3168077497371934,"score_spread":0.2839712923481492,"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."}}