AI-powered
podcast player
Listen to all your favourite podcasts with AI-powered features
The Effects of Distribution Shifts on Test Time Adaptation
The first one was TTM domain, SIFTAware, pass normalization in test time adaptation. Basically, this comes from the perspective that at test time, or when you deploy an actual model, should have distribution shifts are kind of natural. This ties nicely with a federated setting where you could have new clients joining in and maybe they have a new device, a new mobile phone, and the camera sensor is a bit different. And then you can learn to basically interpolate appropriately between the statistics you have on your clean data and the statistics that you've got on corrupted data.