LipGAN
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
Mobile-optimized segmentation backbones leveraging Mixed Depthwise Convolutions for multi-scale feature extraction.
MixNet is a family of mobile-scale convolutional neural networks that utilize Mixed Depthwise Convolutions (MDConv) to achieve superior efficiency and accuracy. Developed by Google Research, MixNet addresses the limitations of standard depthwise convolutions by mixing multiple kernel sizes (e.g., 3x3, 5x5, 7x7) within a single convolution operation. This architecture allows the model to capture high-resolution patterns and low-resolution context simultaneously without the massive parameter overhead of traditional ensembles. When applied to semantic segmentation tasks—often integrated with heads like DeepLabV3+ or Lite-RASP—MixNet provides a lightweight yet powerful backbone that outperforms MobileNetV3 and MnasNet. In the 2026 market, MixNet remains a critical reference architecture for edge-based AI, particularly in autonomous systems and real-time mobile applications where compute budgets are constrained. Its technical architecture is specifically tuned for hardware accelerators that support grouped convolutions, making it a preferred choice for developers building on Snapdragon, Apple Silicon, and Google Tensor chips.
Splits channels into groups and applies different kernel sizes (3x3 to 9x9) to each group in a single op.
Advanced speech-to-lip synchronization for high-fidelity face-to-face translation.
The semantic glue between product attributes and consumer search intent for enterprise retail.
The industry-standard multimodal transformer for layout-aware document intelligence and automated information extraction.
Photorealistic 4k upscaling via iterative latent space reconstruction.
Verified feedback from the global deployment network.
Post queries, share implementation strategies, and help other users.
Network architecture optimized via Neural Architecture Search (NAS) to balance accuracy and latency.
Uses the non-monotonic Swish activation function for smoother gradient flow.
Includes MixNet-S, MixNet-M, and MixNet-L variants designed for specific FLOP counts.
Supports dilated kernels within the mixed framework for larger receptive fields without resolution loss.
Architecture optimized for XLA and TVM compilers.
Provides the search space for developers to run custom NAS for specific hardware.
Real-time road and obstacle segmentation on low-power embedded hardware.
Registry Updated:2/7/2026
Background removal and person segmentation for real-time video effects on mobile devices.
High-accuracy segmentation of anomalies in endoscopy video feeds.