- Recent advances in machine olfaction have demonstrated deep learning algorithms' capabilities in mining patterns in chemosensor data [1, 2]. While these algorithms can perform effective automated feature extraction, they are invariably dependent on a large amount of data. However, there is a pressing need to develop olfactory systems that learn rapidly and adapt continuously in time-critical applications such as gas leak monitoring or fire detection. The primary objectives of this work include the demonstration of rapid-learning and generalization capabilities on chemical sensor data. A simple application is considered in this direction where a system's readiness for rapid classification of gas mixtures is tested. The system consists of a low-cost metal-oxide sensor array that responds to gas mixtures from a headspace. The application in focus is the binary classification of gas sensor data (beverage vs. air). The primary choice of model for algorithm development is a convolutionalRecent advances in machine olfaction have demonstrated deep learning algorithms' capabilities in mining patterns in chemosensor data [1, 2]. While these algorithms can perform effective automated feature extraction, they are invariably dependent on a large amount of data. However, there is a pressing need to develop olfactory systems that learn rapidly and adapt continuously in time-critical applications such as gas leak monitoring or fire detection. The primary objectives of this work include the demonstration of rapid-learning and generalization capabilities on chemical sensor data. A simple application is considered in this direction where a system's readiness for rapid classification of gas mixtures is tested. The system consists of a low-cost metal-oxide sensor array that responds to gas mixtures from a headspace. The application in focus is the binary classification of gas sensor data (beverage vs. air). The primary choice of model for algorithm development is a convolutional neural network due to its promising inferencing capabilities. Owing to these algorithms' data-hungry nature, a partial meta-learning approach, known as transfer learning, is adopted. A baseline convolutional neural network is trained on the sensor data to distinguish beverages from the air. This baseline model is fine-tuned on new beverages, referred to as novel classes, using a one-shot and a five-shot regime. Results show that the fine-tuned models successfully distinguish new beverages from a minimal amount of data, besides overcoming the challenges posed by the chemical sensors, such as short-term and long-term drifts in the measurements. The resulting models perform favourably with an average test accuracy of 0.9165 for one-shot learning and 0.9170 for five-shot learning, given that the average baseline model test accuracy is 0.9356. Despite fine-tuning on novel classes, the model preserves its generalizability and is immune to catastrophic forgetting, a shortcoming often faced due to iterative training of neural networks.…
