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Given two well-trained networks that may have different\narchitectures that handle different tasks, our method aligns the layers of the\noriginal networks and merges them into a unified model by sharing the\nrepresentative codes of weights. The shared weights are further re-trained to\nfine-tune the performance of the merged model. The proposed method effectively\nproduces a compact model that may run original tasks simultaneously on\nresource-limited devices. As it preserves the general architectures and\nleverages the co-used weights of well-trained networks, a substantial training\noverhead can be reduced to shorten the system development time. 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