{"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":"/census/doubleconv","entry":"DoubleConv","source":"Syntology differential census (groundwork/55, run v2_2026-09-22), per sample; not an archive number","census_date":"2026-09-22","battery_sha256":["b986f7e04d794a0d88ad4c5f32cf63ec3590b5deff0192150737bc5f1c0b4677"],"runner_sha256":["5a452d0e7c0da5b80771d1be2afe3572e253568cf5c5d59a0d08ebd663d00808"],"bucket_key":"positional (rank, kind, dtype) of each array argument; the argument name is not part of the key because the harness draws the shared array from (rank, kind) and casts it to the dtype, whatever the name","bucket_fields":["rank","kind","dtype"],"claim":"Implementations sharing this entry name were each run on one shared input fixed by the positional (rank, kind, dtype) of their array arguments (the bucket). A cluster is the set whose recorded output digest (sha256 of the output rounded to 6 decimals) is identical. Identical values to six decimals on the shared input are agreement on those inputs, not a statement about the whole domain and not a substitution claim.","n_implementations_compared":8,"n_papers":7,"n_buckets":1,"n_distinct_outputs":6,"n_class_bearing":8,"not_compared":{"not_run_on_shared_input":{"n":0,"by_error":{}},"no_array_argument_ran_on_own_fixture_arguments_only":{"n":0,"n_papers":0,"recorded_shared_digest_equals_own_fixture_digest":0},"output_not_digested_non_numeric":{"n":0,"by_type":{}}},"withdrawn_excluded":0,"code_page":"/code/doubleconv","buckets":[{"bucket":[[4,"float","float32"]],"n_implementations_compared":8,"n_papers":7,"n_not_digested":0,"not_digested_by_type":{},"clusters":[{"output_sha":"195a614a1570680c","size":2,"n_papers":2,"shape":[2,4,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":2,"torch_reference_conventions_with_this_digest":[],"values":[0.14810490608215332,0.0,0.6865729689598083,0.7094539999961853,1.1497364044189453,0.08211465179920197,0.0,1.052537202835083,0.0,0.0,2.024960994720459,1.0229989290237427,0.0,0.0,0.32147425413131714,0.412229984998703,0.26751548051834106,1.2989022731781006,0.0,0.0,0.0,0.0,0.27402034401893616,0.0,0.0,0.8807365894317627,1.2557514905929565,0.0,0.0,0.4648422300815582,0.0,2.5752313137054443,0.5525316596031189,0.6443008780479431,0.13871882855892181,0.0,0.8373785018920898,0.8245303630828857,0.0,0.0,0.0,0.0,1.0603749752044678,1.6004682779312134,1.2534470558166504,0.0,0.012033463455736637,0.0,0.0,0.0,0.14704443514347076,0.0,0.0,1.1063352823257446,0.19465795159339905,0.8494111895561218,0.0,0.0,0.0,1.5976322889328003,0.4784674644470215,0.0,0.0,1.0617543458938599,0.41824835538864136,0.0,0.6428259611129761,0.9235495924949646,0.2851709723472595,0.0,0.9192970991134644,1.1756051778793335,0.3668130934238434,0.0,0.532156765460968,0.0,0.0,0.0,0.0,0.0,0.0,0.5282377004623413,0.0,0.0,0.0,0.0,1.4786040782928467,0.0,0.0,0.0,0.8726515173912048,1.3348064422607422,0.0,0.0,1.32723867893219,0.8690722584724426,0.32543978095054626,0.7445098757743835,0.9812138676643372,0.01882350631058216,0.2431897521018982,0.0,0.0,0.0,0.0,1.120295763015747,1.698797345161438,0.0,1.0899429321289062,0.3311596214771271,0.0,0.0,0.0,0.766873300075531,0.0,1.6869947910308838,0.23415520787239075,0.91851806640625,0.21586750447750092,0.5975604057312012,0.0,1.2993022203445435,0.2559216618537903,0.0,0.0,0.0,0.14532159268856049,0.9100934863090515],"values_recorded":128,"members":[{"code_sha256_prefix":"3bc37018ec7a5a7e","path":"model.py","papers":["2402.12688"],"paper_pages":[{"arxiv_id":"2402.12688","page":"/paper/robust-wide-robust-watermarking-against"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true},{"code_sha256_prefix":"f416b4cf5c864b4e","path":"demo/UNet.py","papers":["1505.04597"],"paper_pages":[{"arxiv_id":"1505.04597","page":"/paper/u-net-convolutional-networks-for-biomedical"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]},{"output_sha":"597e83da35923a55","size":2,"n_papers":2,"shape":[2,4,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":0.0,"members_with_recorded_values":2,"torch_reference_conventions_with_this_digest":[],"values":[0.0,0.0,0.0,0.0,0.0,0.0,0.9210277795791626,1.0561062097549438,0.0,0.4626387655735016,0.21991446614265442,0.0,2.05212664604187,1.4350801706314087,1.4160737991333008,0.0,0.11531553417444229,0.0,1.5370979309082031,0.4342253506183624,0.22231850028038025,0.11536038666963577,1.71454656124115,0.0,0.0,0.0,2.2628896236419678,0.0,0.0,1.1110780239105225,0.0,0.0,0.0,0.9085893034934998,0.0,0.8549228310585022,0.12053395062685013,2.108976125717163,0.0,1.2045725584030151,0.0,0.26604223251342773,0.3055730164051056,0.20314626395702362,0.0,0.0,0.0,0.6182634234428406,0.0,0.0,0.0,0.28634777665138245,0.0,0.0,0.0,0.0,2.2544174194335938,1.042759895324707,0.0,0.0,1.1571379899978638,0.0,0.0,1.5776604413986206,0.0,0.0,0.0,0.2853860557079315,0.1678723692893982,0.0,0.0,0.05833020806312561,0.0,0.0,0.7210531234741211,0.043818723410367966,0.9711188673973083,1.067708969116211,0.7158017158508301,1.0824251174926758,0.13781248033046722,0.0,0.5802886486053467,1.4247777462005615,0.05856559798121452,0.0,0.4461219906806946,0.2001413255929947,0.0,0.0,1.9033377170562744,0.08019925653934479,0.0,0.0,0.0,0.0,0.0,0.46360573172569275,0.08117809891700745,1.1688438653945923,0.25235438346862793,1.32270348072052,0.0,1.7661634683609009,0.0,1.712363600730896,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.362892746925354,0.0,0.0,0.11847370117902756,1.4952538013458252,0.0,1.2061126232147217,0.0,0.8865819573402405,1.5084556341171265,0.6775885820388794,0.9725251197814941],"values_recorded":128,"members":[{"code_sha256_prefix":"2d7adb8ea78aecb2","path":"networks/UNet.py","papers":["1505.04597"],"paper_pages":[{"arxiv_id":"1505.04597","page":"/paper/u-net-convolutional-networks-for-biomedical"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true},{"code_sha256_prefix":"c849a67d2a1f51dd","path":"src/model.py","papers":["2601.22707"],"paper_pages":[{"arxiv_id":"2601.22707","page":"/paper/arxiv-2601-22707"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]},{"output_sha":"427c4ec189d4e51f","size":1,"n_papers":1,"shape":[2,4,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.2947275936603546,0.3124274015426636,0.0,0.03977961465716362,0.0,0.05678781867027283,0.09226040542125702,1.1711317300796509,0.0,0.0,0.0,0.0,2.2631685733795166,1.596271276473999,1.5757293701171875,0.0,0.062194038182497025,0.229562446475029,1.8395334482192993,0.414958655834198,1.0603861808776855,1.4093416929244995,1.1641011238098145,0.0,0.0,0.0,1.9197204113006592,0.0,0.0,1.1787017583847046,0.0,0.0,0.5927121639251709,0.5890254974365234,0.0,0.9650798439979553,0.3106919825077057,1.7501046657562256,0.0,1.3638768196105957,0.0,0.1456867754459381,0.46676984429359436,0.17722856998443604,0.0,0.0,0.0,0.6712813973426819,0.0,0.0,0.0,0.3196646273136139,0.0,0.0,0.0,0.0,2.714442491531372,1.1610291004180908,0.0,0.0,1.3044776916503906,0.0,0.0,1.7865642309188843,0.0,0.0,0.0,0.35369354486465454,0.10893239080905914,0.0,0.0,0.14828099310398102,0.0,0.0,0.9586636424064636,0.0,1.5523093938827515,1.0491126775741577,1.2702218294143677,1.1160463094711304,0.0,0.0,0.38392627239227295,1.534127950668335,0.0,0.0,0.24475038051605225,0.0,0.0,0.0,2.3872745037078857,0.0,0.0,0.0,0.0,0.0,0.0,0.4614177644252777,0.0,1.3265557289123535,0.06844411045312881,1.5064738988876343,0.0,1.987007975578308,0.0,2.330263137817383,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.520073652267456,1.6201345920562744,0.0,0.9734606146812439,0.0,0.7841339111328125,1.520009160041809,0.8857723474502563,0.9692630171775818],"values_recorded":128,"members":[{"code_sha256_prefix":"6224b1781438c704","path":"model/unet.py","papers":["2011.05479"],"paper_pages":[{"arxiv_id":"2011.05479","page":"/paper/forestnet-classifying-drivers-of"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]},{"output_sha":"59450a11473a719c","size":1,"n_papers":1,"shape":[2,4,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[0.2650553584098816,0.0,0.0,0.0,0.41166841983795166,0.0,0.24769066274166107,1.8565324544906616,0.0,0.4786459803581238,0.8184992671012878,0.0,1.5315403938293457,0.2503982484340668,0.7504489421844482,1.564980387687683,0.17246632277965546,0.0,0.003760912921279669,1.1137012243270874,0.029167111963033676,0.5628373622894287,1.112998366355896,0.0,0.0,0.0,1.680894374847412,1.0945334434509277,0.0,0.9889394044876099,0.0,0.2224593311548233,0.0,0.0,0.0,0.3375805616378784,1.5382417440414429,2.344181776046753,0.0,0.0,0.0,0.22973525524139404,1.0526121854782104,0.21022982895374298,0.26608049869537354,1.1019808053970337,0.0,0.0,0.0,0.37159043550491333,1.4620317220687866,0.38033273816108704,0.0,0.0,0.0,0.5237312316894531,0.5795642733573914,0.9977401494979858,0.0,0.09705930948257446,1.3017542362213135,0.0,0.0,0.22793544828891754,0.8682535886764526,0.0,0.0,0.0,0.0,0.0,0.0,0.7536243200302124,0.0,0.0,0.9531205296516418,0.17325258255004883,0.0,1.4976670742034912,0.7787389755249023,0.0,0.0,1.1731061935424805,0.2941633462905884,0.0,1.551422119140625,0.0,0.05625993758440018,0.34820833802223206,0.0,0.23833511769771576,2.1036694049835205,0.0,0.0,0.1547631174325943,0.0,0.02481139823794365,0.0,0.0,0.0,0.0,0.0,0.9375242590904236,0.0,1.5718673467636108,0.7480757832527161,1.1951711177825928,0.9233630895614624,0.0,0.2922143042087555,0.0,0.0,0.3940810263156891,1.3851213455200195,0.0,0.24476073682308197,0.5191215872764587,0.0,0.0,0.0,0.9158222675323486,1.5877184867858887,0.0,1.4119040966033936,0.6933619379997253,0.0,0.0,0.20289795100688934,0.7052276134490967],"values_recorded":128,"members":[{"code_sha256_prefix":"d7ae316fec313cd2","path":"src/models/fluxnet_d_2d.py","papers":["2602.01941"],"paper_pages":[{"arxiv_id":"2602.01941","page":"/paper/arxiv-2602-01941"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]},{"output_sha":"5b07d37cfc90a44b","size":1,"n_papers":1,"shape":[2,4,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,1.344251275062561,1.2472889423370361,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,2.9180595874786377,2.2017951011657715,2.296006441116333,-0.54354327917099,-0.5710296630859375,-0.5710296630859375,0.708662748336792,-0.5710296630859375,0.16759419441223145,0.3219578266143799,2.7240748405456543,-0.5710296630859375,-0.5710296630859375,-0.4112083613872528,2.7355098724365234,-0.5710296630859375,-0.5710296630859375,1.961006760597229,-0.5710296630859375,-0.5710296630859375,-0.5724225640296936,-0.22972406446933746,-0.5724225640296936,-0.5531889796257019,0.1542656570672989,3.2373926639556885,-0.5724225640296936,1.0235216617584229,-0.5724225640296936,-0.4538721442222595,0.13079482316970825,-0.5724225640296936,-0.5724225640296936,-0.5724225640296936,-0.5724225640296936,0.5878778100013733,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,0.9256272912025452,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,-0.49703115224838257,2.2651216983795166,1.4795000553131104,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,2.360811471939087,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.4806024432182312,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,-0.54354327917099,1.0717189311981201,0.9558942317962646,0.9470832943916321,-0.5710296630859375,-0.5710296630859375,-0.2662985622882843,0.08940563350915909,-0.0857786238193512,-0.5710296630859375,0.5929098725318909,-0.5710296630859375,-0.5710296630859375,-0.12846031785011292,2.4135797023773193,-0.5710296630859375,-0.5710296630859375,-0.5444216132164001,-0.5710296630859375,-0.5710296630859375,-0.5724225640296936,-0.5724225640296936,-0.5724225640296936,0.15956547856330872,0.5068857669830322,1.5153467655181885,-0.5724225640296936,1.986440896987915,-0.39553916454315186,2.633840322494507,-0.5724225640296936,-0.5724225640296936,-0.5724225640296936,-0.5724225640296936,-0.5724225640296936,-0.5724225640296936,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,-0.5894807577133179,0.42527198791503906,0.88536536693573,-0.5894807577133179,2.6702048778533936,-0.23134800791740417,-0.5894807577133179,0.626859724521637,-0.5894807577133179,1.4687124490737915],"values_recorded":128,"members":[{"code_sha256_prefix":"22f2b19060fcec6b","path":"models/unet3dhybrid_dynamicfusion.py","papers":["2602.00995"],"paper_pages":[{"arxiv_id":"2602.00995","page":"/paper/arxiv-2602-00995"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]},{"output_sha":"cbcad76916059b3e","size":1,"n_papers":1,"shape":[2,16,4,4],"dtype":"float32","type":"Tensor","finite":true,"max_difference_among_recorded_values":null,"members_with_recorded_values":1,"torch_reference_conventions_with_this_digest":[],"values":[1.0872002840042114,-0.21495762467384338,-0.24161267280578613,0.020011352375149727,0.5162336826324463,1.5418531894683838,0.5146940350532532,0.6837957501411438,-0.06740414351224899,-0.01772170513868332,0.19407187402248383,1.3039608001708984,-0.05327798053622246,0.09815993160009384,2.9578118324279785,0.22994670271873474,-0.06735307723283768,0.7311824560165405,0.6653395295143127,0.1796741634607315,0.642301082611084,-0.03883151337504387,0.06585212051868439,-0.3824871778488159,0.5548543334007263,-0.236870676279068,1.0805741548538208,0.12041858583688736,1.9768950939178467,-0.012703986838459969,1.0016329288482666,0.5596572160720825,-0.18249017000198364,-0.25835156440734863,0.37779325246810913,0.6046928763389587,-0.1954813003540039,0.25361451506614685,1.1399005651474,0.3592820167541504,-0.34929895401000977,0.6579429507255554,1.037534475326538,0.9316776990890503,-0.0872330442070961,0.43099266290664673,1.5471726655960083,-0.1328364610671997,-0.06653296202421188,1.6079175472259521,1.1278458833694458,-0.13382311165332794,-0.1496599167585373,0.5897952318191528,-0.09788691997528076,-0.18035589158535004,-0.03554142639040947,0.37773647904396057,1.2243574857711792,-0.3392648994922638,1.2826473712921143,-0.15372994542121887,1.815647006034851,0.799357533454895,-0.05621231347322464,-0.035850923508405685,0.28846368193626404,-0.31458279490470886,-0.1167081817984581,-0.11781390011310577,-0.13273869454860687,1.4670330286026,-0.21845011413097382,-0.41643667221069336,-0.4023132920265198,-0.14981429278850555,0.3873569071292877,-0.19762203097343445,-0.18181508779525757,-0.0885767936706543,0.15727713704109192,-0.004609947092831135,1.834462285041809,0.7497319579124451,-0.1666705459356308,-0.15732160210609436,-0.18643967807292938,-0.12864910066127777,0.5619805455207825,2.9274797439575195,-0.20851929485797882,0.834133505821228,-0.02971658669412136,0.9692622423171997,-0.12168186902999878,0.746581494808197,-0.04672132804989815,-0.22217130661010742,-0.04141620546579361,0.587024986743927,-0.17572617530822754,-0.1709812581539154,-0.13451145589351654,-0.04016862437129021,-0.16740836203098297,1.3963137865066528,-0.19502350687980652,-0.326031357049942,-0.08444210141897202,-0.17543001472949982,0.25331565737724304,1.8644274473190308,-0.19632278382778168,1.0218185186386108,0.4960933327674866,0.15019448101520538,0.40880823135375977,1.7544087171554565,-0.3782203197479248,-0.031086474657058716,-0.1359112709760666,-0.09484019130468369,-0.2590453326702118,-0.028820449486374855,0.20177316665649414,-0.021838625892996788,-0.29409676790237427,-0.041529010981321335,0.29509997367858887,1.0664438009262085,0.8796629905700684,0.9532020688056946,0.08064854145050049,1.2532480955123901,-0.1792846918106079,0.10390502214431763,0.11160819977521896,0.687536895275116,0.4519613981246948,0.8230089545249939,1.3532018661499023,1.4775991439819336,0.27031418681144714,1.6723592281341553,-0.17329023778438568,-0.29533621668815613,-0.06801784783601761,0.23433558642864227,0.5508137941360474,0.05639741197228432,0.5168378353118896,0.38518598675727844,0.0030385409481823444,0.3718899190425873,1.296318531036377,0.09082385152578354,1.1216169595718384,-0.017181366682052612,-0.392299622297287,-0.2416645586490631,0.19063757359981537,1.6016087532043457,-0.03476622328162193,0.4232228696346283,0.45953989028930664,0.3158119320869446,0.7479178309440613,0.3261842727661133,0.173137828707695,0.41997507214546204,1.0750880241394043,-0.1366259902715683,0.22247375547885895,0.04932229965925217,1.1419563293457031,0.6737471222877502,-0.11112209409475327,-0.4160843789577484,0.6877227425575256,-0.07837221026420593,-0.1638192981481552,-0.1386355608701706,-0.14869116246700287,-0.22589050233364105,-0.014478829689323902,0.9072094559669495,0.3960302472114563,-0.2663864493370056,1.5721591711044312,1.1446189880371094,1.0472391843795776,0.21644848585128784,0.846005380153656,0.8648998141288757,-0.17175254225730896,-0.0965602844953537,-0.21626149117946625,0.1461632400751114,-0.039069436490535736,-0.09657186269760132,-0.23271988332271576,-0.19676682353019714,-0.5744905471801758,-0.0015117733273655176,0.7524433732032776,0.7022691369056702,0.8735374212265015,-0.13358967006206512,-0.1043936163187027,1.5201776027679443,0.8913012146949768,-0.06988804787397385,0.4584451913833618,-0.08536329120397568,-0.3133237659931183,1.3260418176651,0.23028968274593353,-0.04126366227865219,-0.2462223768234253,-0.011465937830507755,-0.18872907757759094,0.6493290662765503,-0.0006610908312723041,-0.1342305988073349,-0.08934763073921204,-0.327831506729126,0.45070862770080566,0.8134180903434753,-0.13885800540447235,-0.04661208391189575,1.974549651145935,-0.2662220895290375,0.7387345433235168,-0.37664955854415894,-0.3133191764354706,0.6581684947013855,0.19325824081897736,1.5565942525863647,-0.08789826184511185,-0.24024824798107147,-0.0646887868642807,0.5130218863487244,0.9792594313621521,-0.1862020343542099,-0.2761189639568329,-0.03849000856280327,1.5338680744171143,-0.3384084105491638,-0.09522178024053574,-0.11265583336353302,-0.3977024257183075,-0.7407343983650208,0.372640460729599,0.4886932373046875,1.2594338655471802,0.2775985300540924,-0.10067345947027206,-0.16614918410778046,0.8081930875778198,-0.08027216047048569,0.9404140710830688,-0.062103595584630966,0.9017448425292969,0.3194989562034607,-0.03264648839831352,1.087216854095459,-0.012862229719758034,-0.23575173318386078,1.3191492557525635,0.06849981099367142,-0.05869128927588463,-0.11395236104726791,0.6716179847717285,-0.22705446183681488,1.0101447105407715,0.27380090951919556,0.6144329905509949,-0.16844157874584198,-0.200759157538414,0.5697958469390869,-0.11069235950708389,-0.04058469086885452,2.3425402641296387,-0.3696812689304352,1.452196717262268,0.058950018137693405,0.7909236550331116,-0.05289540812373161,-0.11418820917606354,0.673718273639679,-0.1433071345090866,1.068229079246521,-0.03640313073992729,0.287170946598053,-0.06271195411682129,0.44319090247154236,-0.31609103083610535,0.4907834529876709,-0.20310966670513153,1.220428705215454,-0.12944094836711884,0.48456454277038574,1.4628711938858032,-0.00010964780813083053,-0.01705924980342388,0.496536523103714,-0.12851715087890625,0.3542269468307495,0.015180078335106373,-0.5174118876457214,0.9279371500015259,1.391190767288208,-0.16669176518917084,1.071051836013794,2.7160661220550537,0.4625464975833893,1.0035805702209473,2.8502094745635986,1.389784574508667,1.2823843955993652,-0.28478190302848816,-0.0049142129719257355,-0.1311294138431549,-0.012820928357541561,-0.2832995653152466,-0.16920410096645355,0.9588647484779358,-0.04852249473333359,-0.19549553096294403,-0.14510129392147064,0.04491937905550003,-0.198068767786026,-0.010246607474982738,0.42168983817100525,-0.28166016936302185,-0.27315279841423035,-0.2531452775001526,0.2675304710865021,-0.13865169882774353,-0.004474612884223461,0.3032122552394867,0.48274779319763184,-0.015460043214261532,1.6930550336837769,1.0403592586517334,1.8066685199737549,1.6020766496658325,0.5565745234489441,1.2657405138015747,1.486285924911499,0.5727227926254272,0.4140748977661133,-0.10778260231018066,-0.06733650714159012,-0.09275425225496292,-0.08898846060037613,-0.17508827149868011,0.5297191739082336,-0.31118422746658325,-0.2835823893547058,-0.08843805640935898,-0.4299570620059967,-0.47203564643859863,0.11441197246313095,-0.06234675273299217,-0.0613178126513958,1.3035129308700562,0.05361616238951683,-0.017047761008143425,1.2132145166397095,0.05297258123755455,-0.1078060045838356,0.9310126304626465,-0.2502598464488983,-0.06712441891431808,-0.22798693180084229,-0.10274244844913483,-0.0768672451376915,0.47364485263824463,-0.13544391095638275,-0.034433286637067795,-0.2617656886577606,-0.30519014596939087,-0.03601793199777603,0.20570124685764313,1.3503206968307495,0.45255744457244873,0.476547509431839,0.51106858253479,-0.028899019584059715,1.5291515588760376,0.31896448135375977,0.2520543038845062,1.464713454246521,0.5265924334526062,-0.07912648469209671,1.559666395187378,0.2248072475194931,-0.023104941472411156,0.7405349016189575,-0.22652609646320343,-0.019990524277091026,-0.046624816954135895,-0.0376550666987896,0.040012989193201065,0.07444580644369125,1.9088101387023926,0.5512687563896179,0.7849711179733276,1.3521482944488525,-0.11483587324619293,-0.20945151150226593,0.3762734532356262,-0.21787643432617188,-0.4172455370426178,-0.29213324189186096,0.3559522032737732,0.9359171986579895,2.005328893661499,0.9590485692024231,0.5493601560592651,1.7453511953353882,-0.0009577205637469888,1.3123841285705566,0.154727503657341,0.29172781109809875,0.996428370475769,0.3987433612346649,-0.029339252039790154,-0.0725058913230896,-0.002838528249412775,-0.22321827709674835,-0.15365837514400482,-0.1597059965133667,-0.3278963565826416,-0.06679095327854156,-0.007577888667583466,-0.277200311422348,0.6277768611907959,0.9652947783470154,-0.18297411501407623,0.7074300050735474,0.8810292482376099,-0.0184640996158123,0.19904080033302307,1.030545949935913,-0.01361752487719059,-0.1962144821882248,-0.07385944575071335,1.8413786888122559,0.5593658685684204,0.20783446729183197,1.0898959636688232,1.453418493270874,-0.26245230436325073,0.38851693272590637,-0.17665930092334747,-0.10937052965164185,-0.0932813510298729,-0.10634364932775497,-0.1162552461028099,-0.10202803462743759,0.5284686088562012,0.16154415905475616,0.380794882774353,1.1417582035064697,0.663478434085846,1.2478275299072266,-0.04426591843366623,0.6162459254264832,1.5034410953521729,-0.007731946650892496,1.5020992755889893,0.2218492329120636,0.20010671019554138,-0.45763787627220154,1.1534734964370728,-0.10872393101453781,-0.0750211700797081,0.4272986650466919,-0.21889781951904297,-0.2869689464569092,-0.07754873484373093,0.2781974971294403,-0.06231040880084038,0.6139779686927795,-0.45564961433410645,0.044964347034692764,-0.19350025057792664,-0.41459494829177856,0.07117655128240585,-0.06091999635100365,0.26263874769210815,-0.22348693013191223,-0.24439111351966858,-0.033437903970479965,-0.12323462218046188,0.5284338593482971,0.15056708455085754,0.37458527088165283,-0.0569641999900341,-0.08342587202787399,-0.6034579873085022,-0.29678189754486084,-0.050262343138456345,-0.7527787089347839,-0.3012525737285614,-0.04604794457554817,0.5074059367179871,0.3493601679801941,0.5642387866973877,0.9788245558738708],"values_recorded":512,"members":[{"code_sha256_prefix":"adc9b6edca450d82","path":"models/cover.py","papers":["2506.20850"],"paper_pages":[{"arxiv_id":"2506.20850","page":"/paper/vector-contrastive-learning-for-pixel-wise"}],"arg_sig_recorded":[["x",4,"float","float32"]],"scalar_args":{},"class_bearing":true}]}]}]}