Explore a groundbreaking approach to wireless sensing in this 17-minute conference talk from USENIX NSDI '23. Delve into SLNet, a novel deep wireless sensing architecture that combines learning-based spectrogram generation with spectrogram learning. Discover how this innovative method overcomes the time-frequency uncertainty limitation and utilizes a polarized convolutional network to learn both local and global features from spectrograms. Examine the application of SLNet in four real-world scenarios: gesture recognition, human identification, fall detection, and breathing estimation. Learn how this new technique outperforms state-of-the-art models in accuracy while maintaining a smaller model size and lower computational requirements. Gain insights into the potential widespread applications of SLNet's techniques beyond WiFi sensing, opening new possibilities in the field of wireless technologies.