Over at Google Research Blog, they have shared insight into the Artificial
neural networks have spurred remarkable recent progress in image classification and speech recognition. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don't. How do you check that the network has correctly learned the right features? One way to visualize what goes on is to turn the network upside down. Neural
Nnetworks
bthat we
re train
g developed
to discriminate between dif
ferent kinds o
rf image
recognition, as
well as creation, wha
t thve
y call "Inceptqui
onism."te Using a
bit of
the information need
bed to genera
ckte limages too
. It can help
fto visualize the networ
k's repr
oces
sentation. in
g some cases,
this reveals that the neural net
work is
n't abquite l
ooking for the t
o disthing
we thou
ght i
t was
.
If we ch
oose higher-level layers, complex features
and objects, and amplify or
laye
rven simiwhol
ar image
objects
tend to
cre
ate abstract im
ages. After
repeatedly filtering
and re
proc. We
ssing call th
e data, the 'dreams' generated from pure random nois
technique
ar"Ince
ptionism". trulyIf a
beautifucl
insight into
the minud
lo
foks a
machli
ne.
art
ictle
: bit like a bird, the network will make itp://g look more like a bird. After several passes, a highly detailed bird appears, seemingl
y out of nowhere
. Of course
, we ca
n do mor
e than c
h.blo
gspoud wat
.c
om/2015/06/inceptionism-gohing
- with this technique. For example, horizon lines tend
to get filled with towers and pagodas. Rocks and tree
pes tur
-n into
buildings. Birds and insects appear in images of leaves. We can even start this process from a random-
noise image, so that the result becomes purely the result of the neural
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