Swin Transformer V2 - Paper explained

Sdílet
Vložit
  • čas přidán 29. 08. 2024

Komentáře • 7

  • @antonioperezvelasco3297
    @antonioperezvelasco3297 Před 10 měsíci +1

    Really nice presentation! Thank you for time spent!

  • @shilashm5691
    @shilashm5691 Před rokem

    Main branch output is added with non-norm values, not with normalized values

  • @65Jabulani
    @65Jabulani Před rokem

    Very good explanation! Thank you. I also didn't understand the examples regarding the extrapolation rate from the paper, which you presented at 15:17. I can see why they did it, it makes sense to use log. But I don't really understand how they got those numbers.

    • @soroushmehraban
      @soroushmehraban  Před rokem +1

      Yeah the computation doesn’t add up, but it’s a clever way of handling the issue though. Glad you enjoyed it!

  • @Prsahp
    @Prsahp Před rokem

    🔥🤝🏻

  • @alihadimoghadam8931
    @alihadimoghadam8931 Před rokem

  • @EngineerXYZ.
    @EngineerXYZ. Před 7 měsíci

    Is it possible to merge depthwise separable convolution with Swin V2 - Base to reduce parameters and to making feasible to deploy into edge divices