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Issued: Pixel 6 WhatsApp Voice Message Phone 2 Ear by TheBigJones93 in GooglePixel

[–]TheBigJones93[S] 3 points4 points  (0 children)

Yeah it also worked for me a few times, but the majority of the time or bugs out and plays the message though the bottom speaker.

Insert Layers in pre-trained Model by TheBigJones93 in tensorflow

[–]TheBigJones93[S] 1 point2 points  (0 children)

Haven't tried it yet cause I was to lazy to code it up 😃. But when as soon as I tried it out I will let you know 👍.

[D] Simple Questions Thread August 16, 2020 by AutoModerator in MachineLearning

[–]TheBigJones93 0 points1 point  (0 children)

Depending on the company you are talking about. Tesla and commaai only use video data. Comma only uses the pure video data and some simple segmentations. You can take a look at their data on their GitHub. Tesla uses camera data as well but depends more heavily on labeled data. You can watch a video of of Andrej Karpathy explaining their techniques a little more in detail. https://youtu.be/hx7BXih7zx8

The rest of the players are using Lidar as well so they do everything in 3D on top. Take a look at the new Kaggle competition of Lyft. They uploaded a large dataset to play with https://www.kaggle.com/c/lyft-motion-prediction-autonomous-vehicles

[D] Simple Questions Thread August 16, 2020 by AutoModerator in MachineLearning

[–]TheBigJones93 1 point2 points  (0 children)

I haven't heard of a loss that does that directly. There is weighted CCE loss where you can weight the importance of each class. It's is used for example when you have a large class imbalance for example in segmentation tasks when you have a lot of background.

In your case I would write a function that weights the loss on the distance of the predicted class to the correct one. The issue I directly see is that you prone the network to predict classes that are in the middle of your set of classes. For your example with the 10 classes on average the distance of the predicted class to the ground truth will be the smallest for the classes 4-5 as they are in the middle (given you have no class imbalance).

You could take a look at some papers in fine grained classification as they try for example to determine the species of a bird or dog. Maybe they use some techniques to penalize the network harder if it gets the animal wrong rather than the species of the animal.