
Show HN: OmniNet:- A unified architecture for multi-modal multi-task learning - amandavinci
https://github.com/subho406/OmniNet
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whoisnnamdi
Have not read the paper in much depth yet but this looks like great work,
super interesting. Thanks for sharing.

Question: in the example of prediction on untrained tasks, what exactly hasn't
been trained? The paper talks about video being one of the trained tasks. Did
you simply retrain model without video examples and then test performance?

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subho406
The model was trained on video classification, image qa and image captioning.
Video captioning and video qa is not trained, yet the model shows results on
those tasks.

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absingh31
This one looks interesting, I think this paper would start multiple new
researches towards AGI.

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modi15
Can you explain in a bit layman terms what exactly has been done here ? What I
understood is that there is a single NN trained for multiple tasks - but what
is the benefit ?

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pequalsnp
I found my weekend paper. This sounds cool!

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max_
Not an AI expert, but I wanted to know;

Does this bring us closer to AGI?

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subho406
As an author of this paper, I feel such neural networks are indeed a small
step towards what we call AGI. By learning shared representations across a
variety of tasks makes an AI system more robust to real-world datasets and
makes it easy to adapt to new tasks without having to learn everything from
scratch (which we humans are naturally capable of doing)

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keerthankop
Good work guys, well done.

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mtmail
Preferred original title when submitting links: "Show HN: OmniNet - A unified
architecture for multi-modal multi-task learning"

HN is a bit strict. I'd say "X is all you need" gets less attention from users
than a very technical headline. The most popular submission recently had MITM
in the title
([https://news.ycombinator.com/best](https://news.ycombinator.com/best))

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amandavinci
Updated. Thanks for pointing it out.

