{"id":"W4386076493","doi":"10.1109/cvpr52729.2023.01548","title":"ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders","year":2023,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1324,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"","keywords":"Computer science; Artificial intelligence; Machine learning; Normalization (sociology); Feature learning; Pattern recognition (psychology); Autoencoder; Segmentation; Deep learning; Feature extraction; Ranging; Feature (linguistics); Performance improvement; Engineering","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02219720391790699,"score_gpt":0.2561102292598575,"score_spread":0.2339130253419505,"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."}}