Due to the high price and heavy energy consumption of GPUs, deploying deep models on IoT devices such as microcontrollers makes significant contributions for ecological AI.

Inthemeanwhile, another research route focuses onboosting theadversarially trained models via imposing more direct supervision toregularize thelearned representations.

While training with static demonstrations has shown some promise, we show that such methods fall short for controlling real GUIs due to their failure to deal with real world stochasticity and non-stationarity not captured in static observational data.

This,however, typically ignores cortical heterogeneity and may smooth out sources ofvariation [11,16]inways which limitinterpretation [7].