Overview
Score-based generative modhi inogadzira data nekudzidza gradient yekugovera data - iyo nzira inoita kuti chero sampuli ine ruzha itaridzike senge data chaiyo. Ichi chibodzwa-basa rekuona rinobatanidza mamodheru ekupararira ane stochastic musiyano equation uye inosimbisa akawanda emazuva ano majenareta.
Score-Based Generative Models ndeyekombuta-yekuona workflows inodudzira kana kuburitsa inooneka midhiya yekuongorora, mashandiro, uye kugadzira.
Deep Dive
Panzvimbo pekuenzanisira zvakananga, zvibodzwa-zvakavakirwa modhi dzidza zvibodzwa: gradient yeiyo log-inogoneka density maererano nekuisa. Kuziva kuti ndeipi nzira yekukwenya sampuli kuti uwedzere mukana wayo wakakwana kuburitsa data nyowani. Yang Song uye basa raStefano Ermon ra2019 rakadzidzisa network kuti ifungidzire chibodzwa ichi pamatanho mazhinji eruzha uchishandisa denoising mamaki kufananidzwa, ndokuzogadzira masampuli ane Langevin dynamics - achidzokorodza kutsika chibodzwa uye nekuwedzera ruzha rudiki. Pepa ravo re2021 mamakisi-SDE rakaratidza kuti kupararira uye zvibodzwa-zvakavakirwa modhi zviso zviviri zveimwe chete inoenderera maitiro inotsanangurwa neiyo stochastic musiyano equation. Zvine hutsinye, SDE yega yega ine inoenderana deterministic 'mukana kuyerera' ODE inogovera iwo mamarginals, ichigonesa chaiwo mikana uye nekukurumidza sampling.
Technical Insight
Kufungidzira chibodzwa che data rakachena zvakananga kwakaoma uko data iri shoma, saka modhi inodzidziswa pane data inovhiringidzwa neruzha rweGaussian pazvikero zvakawanda. Denoising zvibodzwa zvekufananidza zvinopa chinangwa chinobatika: mamakisi ekugovera ane ruzha akaenzana negwara reruzha rakakamurwa nekusiyana kweruzha, saka kufanotaura ruzha uye kufanotaura zvibodzwa zvakangofanana. Sampling inogadzirisa reverse-nguva SDE (kana yakaenzana mukana-kuyerera ODE) kutanga kubva kwakachena Gaussian ruzha.
Mastering Score-Based Generative Models
Score-based generative modhi inogadzira data nekudzidza gradient yekugovera data - iyo nzira inoita kuti chero sampuli ine ruzha itaridzike senge data chaiyo. Ichi chibodzwa-basa rekuona rinobatanidza mamodheru ekupararira ane stochastic musiyano equation uye inosimbisa akawanda emazuva ano majenareta. Score-Based Generative Models ndeyekombuta-yekuona workflows inodudzira kana kuburitsa inooneka midhiya yekuongorora, mashandiro, uye kugadzira. Kuvaka kunzwisisa kwakadzama, tora Score-Based Generative Models semuenzaniso wekushandisa, kwete chinhu chimwe chete: tsanangura zvinodiwa, kujekesa fungidziro, uye patsanura izvo zvinogona kuitwa nehurongwa hwakavimbika kubva kune zvichiri kuda kutonga kwenyanzvi.
Mukuita, zvikwata zvakasimba zvinoshandisa Score-Based Generative Models kuenzanirana nezviri kuitika semhando yedata, kusiyana kwemwenje, uye kuenderana kwemazita. Ivo vanonyora zvakajeka maitiro ebudiriro, bvunzo vachipokana ne data rechokwadi uye mafambiro ebasa, uye iterate zvichibva pane zvakacherechedzwa maitiro ekutadza kwete kuhwina-nguva imwe chete yebhenji. Apa ndipo apo kunzwisisa kwe theoretical kunoshanduka kuve kugona kwakasimba pane chigadzirwa, mutemo, uye mashandiro.
Visual AI inogona kuita otomatiki yekuongorora, yekuona, uye yekumaka mabasa pachiyero. Panguva imwecheteyo, kodzero dzeMufananidzo uye kubvumirwa kunogona kuve njodzi dzepamutemo kana hunhu husina kujeka. Nzira yakatsiga ndeyekubatanidza kukurumidza kuyedza nekutonga: mhanyisa vatyairi vendege, tora humbowo, buritsa matanda esarudzo, uye urambe uchivandudza chengetedzo semaitiro emuenzaniso, zvinotarisirwa nemushandisi, uye zvinodikanwa zvekutonga.
Strategic Impact
Visual AI inogona kuita otomatiki yekuongorora, yekuona, uye yekumaka mabasa pachiyero.
Visual AI inogona kuita otomatiki yekuongorora, yekuona, uye yekumaka mabasa pachiyero. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Zvikwata zvekugadzira zvinogona prototype pfungwa nekukurumidza nekudzokororwa kwemaoko mashoma.
Zvikwata zvekugadzira zvinogona prototype pfungwa nekukurumidza nekudzokororwa kwemaoko mashoma. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Mashandisirwo anogona kushandisa masaini emifananidzo nemavhidhiyo ayo aimbove akaoma kugadzirisa.
Mashandisirwo anogona kushandisa masaini emifananidzo nemavhidhiyo ayo aimbove akaoma kugadzirisa. Mukutumirwa kwemhando yepamusoro, izvi zvinoshandurirwa kuita mitemo inoyerwa yekushanda, miganhu yevaridzi, uye tsika dzekudzokorora dzinodzokororwa kuitira kuti zvikwata zvikwire kuvimba pane kukwidza kusajeka.
Real-World Implementation
Noise-Conditional Score Networks (NCSN) inogadzira zviso zvemufananidzo nekutevera akadzidza mamakisi gradients kuburikidza neLangevin dynamics.
Kuvakazve mufananidzo wezvekurapa, senge yakawedzera MRI, uko iyo yakadzidzwa mamaki inoita senge isati yazadza isina kusampled scan data.
Molecular uye mapuroteni dhizaini kugadzirwa mukuwanikwa kwezvinodhaka, kuenzanisa 3D maatomu masisitimu ane mamakisi-akavakirwa diffusion.
Audio waveform synthesis apo zvibodzwa zvemamodhi zvinonzwika zvichienda kukutaura kwakachena kana mumhanzi, senge mudiffusion-based vocoder.
Maitiro Ekuita
Score-Based Generative Models mukuita
Noise-Conditional Score Networks (NCSN) inogadzira zviso zvemufananidzo nekutevera akadzidza mamakisi gradients kuburikidza neLangevin dynamics.
Noise-Conditional Score Networks (NCSN) inogadzira zviso zvekuona nekutevera akadzidza zvibodzwa gradients kuburikidza neLangevin dynamics Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura mhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Score-Based Generative Models mukuita
Kuvakazve mufananidzo wezvekurapa, senge yakawedzera MRI, uko iyo yakadzidzwa mamaki inoita senge isati yazadza isina kusampled scan data.
Kuvakazve mufananidzo wezvekurapa, senge yakakwidziridzwa MRI, uko chibodzwa chakadzidzwa chinoshanda senge isati yazadza zvisina kusampled scan data Matimu anowanzo kuwana mibairo iri nani kana achinge atsanangura zvikumbaridzo zvemhando yepamusoro kumberi, chengetedza nzira yekukwira kwevanhu yemakesi ekumucheto, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye kukanganisa mutengo nekufamba kwenguva.
Score-Based Generative Models mukuita
Molecular uye mapuroteni dhizaini kugadzirwa mukuwanikwa kwezvinodhaka, kuenzanisa 3D maatomu masisitimu ane mamakisi-akavakirwa diffusion.
Molecular uye mapuroteni chimiro kugadzirwa mukuwanikwa kwezvinodhaka, kuenzanisa 3D maatomu ekumisikidza ane zvibodzwa-based diffusion Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura emhando yepamusoro kumberi, chengeta nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Score-Based Generative Models mukuita
Audio waveform synthesis apo zvibodzwa zvemamodhi zvinonzwika zvichienda kukutaura kwakachena kana mumhanzi, senge mudiffusion-based vocoder.
Audio waveform synthesis apo mamodheru anonzwika akananga kutaura kwakachena kana mumhanzi, sepamhepo-based vocoder Matimu anowanzo kuwana mhedzisiro iri nani kana achinge atsanangura hunhu kumberi, chengetedza nzira yekukwira kwevanhu yemakesi emupendero, uye kuteedzera zvese zvakawanikwa zvechigadzirwa uye mutengo wekukanganisa nekufamba kwenguva.
Njodzi & Guardrails
Kodzero dzemifananidzo uye kubvumirwa kunogona kuve njodzi dzepamutemo kana provenance isina kujeka.
Kuita kwemuenzaniso kunogona kusiyanisa kupenya, huwandu hwevanhu, uye nharaunda.
Manyepo enhema anogona kusacherechedzwa kunze kwekunge zvikumbaridzo zvekuvimba zvikatariswa.
Implementation Roadmap
Tsanangura maitiro ekugamuchirwa echokwadi, kurangarira, uye mutengo wekukanganisa.
Tsanangura maitiro ekugamuchirwa echokwadi, kurangarira, uye mutengo wekukanganisa. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Edzai nedata rinoenderana nemamiriro chaiwo ekugadzira.
Edzai nedata rinoenderana nemamiriro chaiwo ekugadzira. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Wedzera ongororo yemunhu kune yakaderera-kusavimbika kana yakakwirira-inokanganisa kufanotaura.
Wedzera ongororo yemunhu kune yakaderera-kusavimbika kana yakakwirira-inokanganisa kufanotaura. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.
Tevera modhi kudonha uye simbisa mushure mekuchinja kwekamera kana dataset.
Tevera modhi kudonha uye simbisa mushure mekuchinja kwekamera kana dataset. Bata nhanho yega yega segedhi rehumbowo: kana maitiro asina kusangana, imbomira kuburitsa, vhara gaka, uye wobva wawedzera kushandiswa.