ShuffleNetV2
PulseAugur coverage of ShuffleNetV2 — every cluster mentioning ShuffleNetV2 across labs, papers, and developer communities, ranked by signal.
1 day(s) with sentiment data
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New deepfake detection frameworks tackle low-resolution and video challenges
Researchers have developed two new frameworks for detecting deepfakes, addressing limitations in current models. The first, AdaGate-DF, is designed for low-resolution and resource-constrained environments by adaptively …
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PrintGuard 2.0 launches with 5MB TFLite model for browser and CPython
PrintGuard 2.0 is an updated system for detecting failures in 3D printing, utilizing a ShuffleNetV2 encoder and a prototypical network for few-shot fault detection. The new version features a significantly smaller Tenso…
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New framework evaluates AI driver models on more than just accuracy
Researchers have introduced a new framework for evaluating driver monitoring models, moving beyond simple accuracy metrics. The Human-Centered Benchmarking Framework (HCBF) assesses models on accuracy, explainability, e…
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New lightweight method improves face image quality assessment
Researchers have developed a new, lightweight method for assessing the quality of face images, which is crucial for face recognition systems. This approach uses an ensemble of two compact neural networks, MobileNetV3-Sm…