AdaMTL: Adaptive Input-dependent Inference for Efficient Multi-Task Learning

• URL: http://arxiv.org/abs/2304.08594v1
• Date: Mon, 17 Apr 2023 20:17:44 GMT
• Title: AdaMTL: Adaptive Input-dependent Inference for Efficient Multi-Task Learning
• Authors: Marina Neseem, Ahmed Agiza, Sherief Reda
• Abstract summary: We introduce AdaMTL, an adaptive framework that learns task-aware inference policies for multi-task learning models. AdaMTL reduces the computational complexity by 43% while improving the accuracy by 1.32% compared to single-task models. When deployed on Vuzix M4000 smart glasses, AdaMTL reduces the inference latency and the energy consumption by up to 21.8% and 37.5%, respectively.
• Score: 1.4963011898406864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modern Augmented reality applications require performing multiple tasks on each input frame simultaneously. Multi-task learning (MTL) represents an effective approach where multiple tasks share an encoder to extract representative features from the input frame, followed by task-specific decoders to generate predictions for each task. Generally, the shared encoder in MTL models needs to have a large representational capacity in order to generalize well to various tasks and input data, which has a negative effect on the inference latency. In this paper, we argue that due to the large variations in the complexity of the input frames, some computations might be unnecessary for the output. Therefore, we introduce AdaMTL, an adaptive framework that learns task-aware inference policies for the MTL models in an input-dependent manner. Specifically, we attach a task-aware lightweight policy network to the shared encoder and co-train it alongside the MTL model to recognize unnecessary computations. During runtime, our task-aware policy network decides which parts of the model to activate depending on the input frame and the target computational complexity. Extensive experiments on the PASCAL dataset demonstrate that AdaMTL reduces the computational complexity by 43% while improving the accuracy by 1.32% compared to single-task models. Combined with SOTA MTL methodologies, AdaMTL boosts the accuracy by 7.8% while improving the efficiency by 3.1X. When deployed on Vuzix M4000 smart glasses, AdaMTL reduces the inference latency and the energy consumption by up to 21.8% and 37.5%, respectively, compared to the static MTL model. Our code is publicly available at https://github.com/scale-lab/AdaMTL.git.

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