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It specifically targets the growing adoption of input-adaptive multi-exit neural networks, which are designed to reduce carbon footprint by passing images through just one neural layer to see if the necessary threshold to accurately report what the image contains has been achieved. In a traditional neural network, the image would be passed through every layer before a conclusion is drawn, often making it unsuitable for smart devices or similar technology that requires quick answers using low energy consumption. The researchers found that by simply adding more complication to images, such as slight background noise, poor lighting, or small objects that obscure the main subject, the input-adaptive model views these images as being more difficult to analyse and assigns more computational resources as a result.

United statesTudor dumitraMaryland cybersecurity centerInternational conference on learning representationInternational conferenceLearning representationஒன்றுபட்டது மாநிலங்களில்மேரிலாந்து இணைய பாதுகாப்பு மையம்சர்வதேச மாநாடு