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Empirical Risk Minimization
Basics
Nhungamiro yehunyanzvi
Sharpness-Aware Minimization (SAM) inzira yekusimudzira isingatsvage kurasikirwa kwakaderera asi kurasikirwa kwakaderera munzvimbo yese yehuremu - hushoma hwakadzika.
Teerera kugwaro iriAI-yakarondedzerwa · ~2 min
Flatter minima tend to generalize better, so SAM often improves test accuracy and robustness without changing the model architecture.
Kudzidziswa kwakajairwa kunoderedza kurasikirwa panzvimbo imwe chete munzvimbo yehuremu, asi mhinduro mbiri dzine kurasikirwa kwekudzidziswa kwakafanana dzinogona kuita zvakasiyana: 'yakapinza' hushoma hunogara mumupata wakamanikana umo huturu hwehuremu hunowedzera kurasikirwa, nepo 'flat' shoma inobvumira kukanganisa uye kazhinji inogadzirisa zvirinani kune data risingaonekwe. SAM, yakaunzwa neGoogle vatsvakurudzi muna 2020, inoita kuti izvi zvive pachena. Panhanho imwe neimwe inotanga yawana huremu hwepedyo (mukati meradius rho) iyo inowedzera kurasikirwa - muvakidzani wakaipisisa - yobva yagadzirisa huremu hwepakutanga kuderedza kurasikirwa panzvimbo iyoyo. Iyi min-max chinangwa inosundira optimization kumatunhu akadzikira zvakafanana, achipa zvinooneka zvirinani kurongwa kwemifananidzo uye kupfuura.
Kuwedzera Vision Transformer uye ResNet kurongeka paImageNet nekudzidziswa neSAM panzvimbo yeSGD yakajeka.
Kuvandudza kusimba kwekuisa ruzha, sezvo flat minima isingawanzo kubata nemusoro mavara akaora.
Kunyatsogadzirisa mamodheru emitauro yakadzidziswa neSAM kuti uwane kujekeswa kuri nani pamaseti madiki ezasi.
Kushandisa ESAM kana LookSAM akasiyana kana iyo yakapetwa kaviri compute mutengo wevanilla SAM uchidhura zvakanyanya.
SAM yakabereka mhuri yekutevera yakanangana nekushaya simba kwayo kukuru, iyo yakapetwa kaviri komputa: yakanyatso siyana seESAM, LookSAM, uye nzira dzinokanganisa chete subset yehuremu kana kushandisa SAM matanho mashoma ega ega. Adaptive SAM (ASAM) inogadzirisa radius kuti ive chikero-chisingachinji. Vatsvaguri vanoramba vachikakavadzana chaizvo kuti sei kupfava kunobatsira uye nekuiyera sei, uye kupinza-kuziva mazano ari kupararira kune kunyatso-tuta mikuru mamodheru emitauro uye nekuvandudza kusimba kune yekugovera shanduko.
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Sharpness-Aware Minimization (SAM) inzira yekusimudzira isingatsvage kurasikirwa kwakaderera asi kurasikirwa kwakaderera munzvimbo yese yehuremu - hushoma hwakadzika. Flatter minima inowanzoita generalize zvirinani, saka SAM inowanzovandudza bvunzo kurongeka uye kusimba pasina kushandura modhi yekuvaka.
SAM inonangidzira flat minima nekuti kurasikirwa kunoramba kwakadzikira pasi pehuremu hudiki huremu hunowanzo wedzera zvirinani kune data risingaonekwe.
SAM inoverengera gradient kuti iwane yakaipisisa-kesi iri padyo poindi, yozoita imwe gradient ipapo yekuvandudza - inopeta kaviri mutengo wakajairika.
Rho inoisa kuti nhanho 'yekukwira' inosvika papi kuti uwane muvakidzani akashata, achitsanangura kuti yakakura sei nzvimbo yakati sandara inotsvakwa neSAM.
SAM inotanga kuvhiringa huremu mukati meradius rho munzira iyo inowedzera kurasikirwa, yobva yadzika kubva panzvimbo yekutanga ichishandisa gradient yakaverengerwa ipapo.
Kubata nemusoro mavara ane ruzha kazhinji kunoda akapinza, nhete minima; nekufarira nzvimbo dzakati sandara, SAM inoramba kuwandisa uku.
Ramba uchidzidza
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InoteveraGaidhi rinotevera
Empirical Risk Minimization
Basics