[HN Gopher] What Inception Net Doesn't See
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       What Inception Net Doesn't See
        
       Author : Aliabid94
       Score  : 26 points
       Date   : 2021-02-15 20:01 UTC (1 days ago)
        
 (HTM) web link (abidlabs.github.io)
 (TXT) w3m dump (abidlabs.github.io)
        
       | keyle wrote:
       | SARAH CONNOR? <Sarah Connor does a handstand>
        
       | Imnimo wrote:
       | The ImageNet-A dataset (https://github.com/hendrycks/natural-adv-
       | examples) contains thousands of hand-curated examples that are
       | challenging for off-the-shelf classifiers. It's interesting to
       | look through them and guess why each one's so difficult.
       | 
       | For examples like the surgical mask, the ImageNet "mask" category
       | is for masquerade masks and halloween masks, I suspect it doesn't
       | contain anything similar to a surgical mask. Imagenet categories
       | are a bit weird that way - they're a pretty arbitrary set of
       | labels that were selected for use as a benchmark, not for
       | practical use. It's got 100 different breeds of dogs, because it
       | was a way to test fine-grained classification. It's got crane
       | (the bird) and crane (the machine). It's got 'car mirror' but not
       | 'mirror'. It's got 'yurt' but not 'house'.
       | 
       | It's great if the categories you care about happen to match the
       | ones on the list (in both name and type, as seen in the 'mask'
       | example). But otherwise you'll quickly run into the need to fine-
       | tune the model on some of your own data to get the categories you
       | want.
        
       | prashp wrote:
       | This seems like a thinly veiled advertisement for Gradio. This is
       | an otherwise trivial observation of convolutional neural
       | networks, given the rapid pace of the field in general
        
       | pool1892 wrote:
       | how is this a new observation in any way?
        
         | glsdfgkjsklfj wrote:
         | I do not think it is supposed to be new. Everyone knows the
         | flaws but downplay it _by a lot_. (negative proofs doesn 't get
         | funding and all that...)
         | 
         | I see this as a very funny way to recall what we always knew.
         | Like a court jester reminding the king he was always naked.
        
           | dwiel wrote:
           | This analysis is quite the strawman. It's a bit like taking a
           | demo SQL table and basic select queries off an intro to SQL
           | tutorial and claiming SQL sucks because the queries are
           | inefficient and the tables can only store books and author
           | names but not any other kind of data because that's what the
           | example was. There is a lot more to SQL than what gets
           | introduced in the intro to SQL tutorial and there is a lot
           | more to computer vision nets than grabbing the network
           | trained on a benchmark dataset and shoving completely
           | different data at it.
        
             | sgt101 wrote:
             | >there is a lot more to computer vision nets than grabbing
             | the network trained on a benchmark dataset and shoving
             | completely different data at it.
             | 
             | Sure, you are right, but lots of people are wandering about
             | saying different!
        
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