 more a collection of elements of generic mammals — eyes, fur, noses — rather than a collection of different classes of animals. We’ve also added labels that show which class each averaged activation most contributes to. Please note, in some areas of a layer this early in the network these attribution labels can be somewhat chaotic. In early layers the attribution vectors have a small magnitude since they don’t have a consistent effect on the output. As we move further down we start to see different types of fur and the backs of four-legged animals. Below this, we find different animal legs and feet resting on different types of ground. Below the feet we start to lose any identifiable parts of animals, and see isolated grounds and floors. We see attribution toward environments like “sandbar” and also toward things that are found on the ground, like “doormat” or “ant”. These sandy, rocky backgrounds slowly blend into beaches and bodies of water. Here we see lakes and oceans, both above and below water. Though the network does have certain classes like “seashore”, we see attribution toward many sea animals, without any visual references to the animals themselves. While not unexpected, it is reassuring to see that the activations that are used to identify the sea for the class “seashore” are the same ones used when classifying “starfish” or “sea lion”. There is also no real distinction at this point between lakes and ocean — “lakeside” and “hippopotamus” attributions are intermingled with “starfish” and “stingray”. Now let’s jump to the other side of the atlas, where we can see many variations of text detectors. These will be useful when identifying classes such as “menu”, “web site” or “book jacket”. Moving upward, we see many variations of people. There are very few classes that specifically identify people in ImageNet, but people are present in lots of the images. We see attribution toward things people use (“hammer”, “flute”), clothes that people wear (“bow tie”, “maillot”) and activities that people participate in (“basketball”). There is a uniformity to the skin color in these visualizations which we suspect is a reflection of the distribution of the data used for training. (You can browse the ImageNet training data by category online: swimming trunks, diaper, band aid, lipstick, etc.) And finally, moving back to the left, we can see round food
more a collection of elements of generic mammals — eyes, fur, nose — preferably than a collection of different classes of animal. We ’ve also added labels that indicate which class each averaged energizing about contributes to. Please note, in some sphere of a layer this early in the network these attribution label can be somewhat chaotic. In early layers the attribution vectors have a small order of magnitude since they do n’t have a consistent effect on the output signal. As we move further down we originate to see unlike types of fur and the back of four - legged animals. Below this, we find different animal leg and feet resting on different types of ground. Below the foot we start to lose any identifiable parts of animals, and see isolated grounds and floors. We see attribution toward environments like “ sandbar ” and besides toward thing that are found on the flat coat, like “ doormat ” or “ ant ”. These sandy, rocky backgrounds lento blend into beaches and consistency of water. Here we see lake and oceans, both above and below water. Though the network does have certain classes like “ seashore ”, we visualize attribution toward many sea animals, without any visual references to the animals themselves. While not unexpected, it is reassuring to interpret that the activations that are used to identify the sea for the class “ seashore ” are the like ones used when classifying “ starfish ” or “ sea lion ”. There is also no real distinction at this compass point between lakes and ocean — “ lakeside ” and “ hippopotamus ” attribution are intermingled with “ starfish ” and “ stingray ”. Now permit ’s jump to the early side of the atlas, where we can see many variations of text detector. These will be useful when identifying course such as “ menu ”, “ web web site ” or “ book jacket ”. Moving up, we see many variations of people. There are very few classes that specifically name people in ImageNet, but people are present in fortune of the images. We see attribution toward things people use (“ hammer ”, “ flute ”), clothes that multitude wear (“ bow tie ”, “ maillot ”) and activities that people participate in (“ basketball ”). There be a uniformity to the hide semblance in these visualizations which we suspect is a reflection of the distribution of the data used for training. (You can shop the ImageNet training data by category online: swimming trunks, diaper, ring aid, lipstick, etc .) And finally, moving back to the left, we can see orotund food
 moge a collection of elemekts of generic mcnmals — eyes, rur, noser — rather than a collection lf difftgent classes of animaus. We’ve apso addee lauels that show wijch class eacg avexajed activation kost contrhbutes to. Pleave nlte, in some areas of a layer this ewrly in tje network thefe autrybutjon labels can be somewhat chaotic. In earny layers the attribution vectors have w smwll magnitude sincf they don’t havq a consisteng effect on the output. As we move further down we staft to see diffetzbt hipes of fur end thv backs of fomg-legged animalx. Below this, wc finv didferent animal legs aid feet resting on dyfferent dy'es of ground. Below tye feet we vtarg to loae ahy idejtihiable parta of animalw, and see isolated brjlmds and floods. We feq attribution toward environments like “vansbar” and also toward thungs that are found oj the grotnd, like “doormat” or “ant”. These sandy, rocky backgrognds auowoy coejd into beaches and bodies of water. Here we sqs kales and oceans, both above ajd felow water. Tfough cge network does have certaig clawses like “seaxhore”, we see attribution toqard many sec abimals, without any visual refzrencex to yhe animals themselves. Chile hot unexpeched, it is feassuring to sed tmat the activations that are tsed to iventiyy the sda fpr the class “seadhore” are the same ones ksed chen wlassifyinh “starfish” or “sea lion”. There is also no real civtitction ac this point betweeg lakes and ocgan — “lakefide” xnd “hippopktamus” ettributions are intermincped with “stacfish” and “stibgrat”. Now ldg’s jump to the other sibt of the arlas, where we can xee jany variations of text detectors. Thdse wpll bq useful when idevtiwuing zlasses sucm ar “memu”, “web site” or “book backst”. Moving upward, wr fee many variatijns of people. There are very fea clavser thay skecifically identify people in JmageNet, hut people are [rescnt yn lots of the images. We see attribution toward tiings people use (“hammet”, “flute”), clothes that keokle wear (“bow vie”, “mayllot”) and activities that people participate ik (“basketball”). There is z unifmrmitj to the skin color in these visualizations which we suspect is a reflectuon of the distribhtiom of nhe bata usqd flc training. (You cam browse the ImageNet training vata by cadeyory online: swimming trunks, doaoer, band aid, uipstick, etc.) And finally, moving nack to the left, we can see roumd food
more a collection of elements of generic eyes, noses — than a collection We’ve added labels that which class each activation most contributes to. Please note, some areas of a layer this early in the network these attribution labels be somewhat chaotic. In early layers the attribution vectors have a small magnitude they have consistent on the output. As we move further down we start to see different types of fur the backs of four-legged animals. Below this, we different animal legs and resting on different types of Below feet we to any parts of animals, see isolated grounds and floors. We see attribution toward environments like “sandbar” and also toward things that found on like “doormat” “ant”. sandy, backgrounds slowly blend and bodies of water. Here we oceans, both above and below water. Though the does have classes like “seashore”, we see attribution many sea animals, without any visual references to animals themselves. While not unexpected, it is reassuring to see that the activations that are identify the sea for class “seashore” are same used classifying or “sea There is also no real distinction at this point between lakes ocean — “lakeside” and “hippopotamus” attributions are intermingled with “starfish” Now jump to the side of the atlas, we see many variations of These be classes as “web site” or “book Moving upward, we see many of people. There are identify people in ImageNet, but people are present lots of the images. We see attribution things people use (“hammer”, “flute”), clothes that people wear (“bow tie”, “maillot”) activities that in (“basketball”). There is a uniformity to the color in these visualizations we suspect is a reflection of the distribution of data for training. can browse the training data by online: swimming trunks, aid, lipstick, And moving left, we can see round food
 more a collection of elements Of generic mAmmalS — eyEs, fUr, NoseS — ratHer than a collecTIon oF different classes of aniMals. WE’vE Also ADdEd labEls that SHoW WHicH cLaSs eAcH AvErageD acTivatioN most contrIbuTeS to. Please notE, In Some areas oF a lAyer this earlY in The netWoRk tHEse atTriButioN labelS Can be sOmewhat chAoTIc. In eaRLy layerS THe AttrIbution vectors havE A sMAll magnitude siNce theY dON’t HAVe a ConSistent effEcT on thE Output. AS We MOVE fuRTher down we staRt to see diffERenT types Of Fur ANd the bAcks oF fOUr-lEgged animalS. BelOw this, we fInd difFErent anIMal legs And feeT reStiNg on DIfFeRenT tYPes OF gRouND. BeLow the feEt We Start To loSE ANY ideNtiFiabLe parTs of animals, anD seE isoLAteD grouNds anD floOrS. We seE attriButioN tOward environmenTs liKe “sandbar” And AlSo tOwArd thINgs thaT arE foUnd on thE ground, LIke “DoORMAt” Or “ant”. These sandy, rocKy BACkGrounds sLowly bLEnD iNTo beacheS aNd bOdieS OF wateR. HerE We See lakes And oceANs, BoTh above AnD below WaTer. thoUgh thE NetwOrk doeS have cerTain cLAsses like “seashORe”, we see attribUTiON ToWArd mAny Sea animals, wIthoUT any VisuAL rEfeREnces To the AnIMaLS themselves. While not UnExpectEd, it iS reassuring to See that the ACTIvations That ARe USed to identify tHe sea For the clasS “Seashore” Are thE same oneS used when CLAssifyinG “stArfIsh” Or “sEA LiOn”. There is also NO Real DiStinctiOn aT this poInt BetWeeN laKeS and ocean — “Lakeside” AnD “hIpPoPotAmus” aTTributioNs Are InTerMinglED with “sTarfiSh” anD “sTiNGraY”. Now let’S JuMP To thE oThEr siDe oF tHe atlAs, whERe wE can see Many variaTioNS of tExT dEtectorS. These will be uSeFul when ideNtIfyIng claSSEs such as “Menu”, “web site” or “book jacket”. mOving upWarD, we seE manY variatioNs oF peoplE. ThERe are vEry few ClassEs ThaT SPecifICAlLy iDeNtify peoplE IN ImAgeNeT, bUt peOple are Present in lots of the IMagEs. We see attribUtiOn toWARd ThiNGs PEopLe USe (“hAMMer”, “flute”), clothes That people WeAR (“bOw tie”, “maillOT”) anD aCtivitiEs that pEople PArticipAte in (“baskEtball”). TheRe Is a uNIForMity to the sKin color In these viSUalizATiOns whIch We suspEcT is A reflEction OF thE distRibutiOn Of the dAta usEd For trainIng. (You can browse the Imagenet traIning DatA by categoRy oNLinE: swimming TrunKs, diaper, baNd aId, lIpstiCk, eTC.) And fInalLY, mOviNG back To thE Left, we can SEe RouND FoOd
 more a collection of elem ents of ge neric  ma mma ls  — e yes,  fur, noses —r athe r than a collection of  diff er e nt c l as ses o f anima l s. W e’v eal soad d ed  labe lsthat sh ow which c las seach average d  a ctivationmos t contribute s t o. Ple as e n o te, i n s ome a reas o f  a lay er this e ar l y in t h e netwo r k  t hese  attribution labe l sc an be somewhat  chaot ic .  I n ear lylayers the  a ttrib u tion ve c to r s hav e  a small magn itude since the y don’ thav e  a con siste nt eff ect on theoutp ut. As we  movef urtherd own westarttosee  dif f er en t t yp e s o f  f ura ndthe back sof  four -leg g e d anim als . Be low t his, we finddif fere n t a nimal  legs  and  f eet r esting  on d if ferent types of  gro und. Belo w t he  fe et  we s t art to  lo seany ide ntifiab l e p ar t s of  animals, and seeis o l at ed groun ds and fl oo r s. We se eatt ribu t i on to ward en vironmen ts lik e  “ sa ndbar”an d also  t owa rdthing s  tha t arefound on  theg round, like “d o ormat” or “an t ”. T he s e sa ndy , rocky bac kgro u ndsslow l yble n d int o bea ch e sa nd bodies of water.  H ere we  seelakes and oce ans, botha b o ve and b elow wa t er. Though the  netw ork does h a ve certa in cl asses li ke “seash o r e”, we s eeatt rib uti o n  t oward many se a anim al s, with out  any vi sua l r efe ren ce s to theanimalsth em se lv es.  Whil e  not une xp ect ed , i t isr eassur ing t o se eth a t t he acti v at i o ns t ha tareuse dto id enti f y t he seafor the c las s  “se as ho re” are  the same one sused whencl ass ifying “ starfish ” or “sea lion”. Therei s alsonorealdist inction a t t his po int betwee n lake s and  o cea n — “la k e si de”  a nd “hippop o t amu s” at tr ibut ions ar e intermingled wit h  “s tarfish” and“st ingr a y ”.  No w  l e t’s  j u mpt o  the other side  of the at la s ,where we c a n s ee  many v ariatio ns of text de tectors.These wil lbe u s e ful  when iden tifyingclasses s u ch as “m enu”,  “w eb sit e”  or  “boo k jack e t”.  Movi ng upw ar d, wesee m an y variat ions of people. There a re ver y few  cl asses tha t s p eci fically i dent ify people  in  Im ageNe t,b ut pe ople ar e p r esent  inl ots of th e  i mag e s .We see attr i b u tio n tow ard things  peo ple use (“hammer” ,  “flute”), clo thes t hat  pe o plewe ar (“bow tie”,  “m ai l l ot”) and  a ctivities t hat peop le parti cipate  in (“ basketb a l l” ) . Ther e is  auniformit y t ot he skin  c ol o r in t hese  v isuali zation s  whi c h  we suspect is a  refl e c tiono f t he di st ributio n  ofthe data u sed for tra ining.  (Yo u can  browse  t he Ima geN et  trainingd ata by ca tegor y onlin e:  swi mmi ng tru nks, d iaper , ba nd  ai d, lipsti c k ,e tc .) And  fin ally,  m ovin g back to the left , w e  can se erou n d  food
 more_a collection_of elements of generic_mammals —_eyes,_fur, noses_—_rather than a_collection of different_classes of animals. We’ve_also added labels_that_show which class each averaged activation most contributes to. Please note, in some areas_of_a layer_this_early_in the network these attribution_labels can be somewhat chaotic._In early_layers the attribution vectors have a small magnitude_since_they don’t have_a consistent effect on the output. As we move_further down we start to see_different types of_fur_and_the backs of four-legged_animals. Below this, we find different_animal legs and feet resting on_different types of ground. Below the feet_we start to lose any identifiable_parts of animals, and see_isolated grounds_and floors. We see attribution_toward environments like_“sandbar” and_also toward things_that are found on the ground,_like “doormat” or_“ant”. These sandy, rocky backgrounds slowly_blend_into beaches and_bodies_of_water. Here_we see lakes_and_oceans, both_above_and below water. Though the network_does_have certain classes like “seashore”, we see_attribution toward many sea_animals,_without any visual references_to the animals themselves. While_not unexpected, it is reassuring to_see that_the activations_that are used to identify the sea for the class “seashore”_are the same ones used when_classifying “starfish” or “sea_lion”. There_is_also no real_distinction_at this_point between lakes and ocean — “lakeside”_and “hippopotamus”_attributions are intermingled with “starfish” and_“stingray”. Now let’s jump_to_the other side of the atlas,_where we can see many variations_of text detectors. These will_be_useful_when identifying classes such as_“menu”, “web site” or “book jacket”._Moving upward, we_see many variations of people. There are_very_few classes that specifically identify people_in_ImageNet, but people are present in_lots_of_the images. We see attribution_toward things people use (“hammer”, “flute”),_clothes that people wear (“bow tie”, “maillot”) and activities_that people participate_in (“basketball”). There is a_uniformity_to_the skin color in these visualizations which we suspect is_a reflection_of the distribution_of the data used for training. (You can browse the_ImageNet training data by category online: swimming_trunks, diaper, band aid, lipstick, etc.) And finally, moving back to_the left, we can see round food