MobileNets introduce depthwise separable convolutions plus width and resolution multipliers to produce efficient CNNs that trade off latency and accuracy for mobile and embedded vision applications.
Xception: Deep learning with depthwise separable convolutions.CoRR, abs/1610.02357
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ANL uses diffusion noise prediction and attention to regularize deepfake detectors for better generalization to unseen synthesis methods without added inference cost.
DeepLabv3 improves semantic segmentation by capturing multi-scale context with cascaded or parallel atrous convolutions and adding global context to ASPP, achieving better results on PASCAL VOC 2012 without DenseCRF post-processing.
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DYMAPIA builds dynamic anomaly masks from Fourier spectra, texture, edges, and optical flow to guide a lightweight DistXCNet classifier, reporting over 99% accuracy and F1 on FF++, Celeb-DF, and VDFD.
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Attention Is All You Need
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