Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for performing interactive digital image editing operations utilizing machine learning models and a feature backpropagation refinement layer. For example, the disclosed systems perform interactive digital image editing operations by incorporating a feature backpropagation refinement layer within a non-interactive machine learning model that utilizes a consistency loss to adjust the feature backpropagation refinement layer according to one or more user interactions. In some embodiments, the disclosed systems utilize a feature backpropagation refinement layer that includes a bias sublayer for localizing changes to a digital image and a convolutional sublayer for channel-wise scale and feature combinations across channels. In some cases, the disclosed systems utilize a consistency loss that facilitates localized modifications to a digital image based on distances of various pixels or features from a user interaction.

Abstract: The present disclosure relates to systems, methods, and non-transitory computer readable media for performing interactive digital image editing operations utilizing machine learning models and a feature backpropagation refinement layer. For example, the disclosed systems perform interactive digital image editing operations by incorporating a feature backpropagation refinement layer within a non-interactive machine learning model that utilizes a consistency loss to adjust the feature backpropagation refinement layer according to one or more user interactions. In some embodiments, the disclosed systems utilize a feature backpropagation refinement layer that includes a bias sublayer for localizing changes to a digital image and a convolutional sublayer for channel-wise scale and feature combinations across channels. In some cases, the disclosed systems utilize a consistency loss that facilitates localized modifications to a digital image based on distances of various pixels or features from a user interaction.

Abstract: A self-cleaning device of a filtering net of air conditioner is installed on a filtering frame in an air inlet of a dust spiral case of a package air conditioner and is disposed outside the filtering net. A filtering net guide rail with slots is installed on an upper frame and a lower frame. The filtering net is fixed on the filtering net frame by the slots of guide rails. The cleaning device of the filtering net includes a sweeping assembly with a brush, a drive motor fixed on the sweeping assembly, a transmission mechanism connected to the drive motor, a rack group installed on the left and right side of the filtering net, a dust collecting box installed under the filtering net for collecting dust, and a dust exchanging device is disposed between the dust collecting box and the filtering net.

Abstract: A self-cleaning device of a filtering net of air conditioner is installed on a filtering frame in an air inlet of a dust spiral case of a package air conditioner and is disposed outside the filtering net. A filtering net guide rail with slots is installed on an upper frame and a lower frame. The filtering net is fixed on the filtering net frame by the slots of guide rails. The cleaning device of the filtering net includes a sweeping assembly with a brush, a drive motor fixed on the sweeping assembly, a transmission mechanism connected to the drive motor, a rack group installed on the left and right side of the filtering net, a dust collecting box installed under the filtering net for collecting dust, and a dust exchanging device is disposed between the dust collecting box and the filtering net.