Convolutional neural network architectures used in this Letter. Left: `Exonet`, where the additions over the baseline `Astronet` model are shown in blue (Section 3.2). The flattened outputs of the disjoint one-dimensional convolutional columns are concatenated with the stellar parameters, then fed into the fully connected layers ending in a sigmoid function. Following Shallue & Vanderburg (2018), the convolutional layers are denoted as CONV–﹩\langle \mathrm{kernel}\,\mathrm{size}\rangle ﹩–﹩\langle \mathrm{number}\,\mathrm{of}\,\mathrm{feature}\,\mathrm{maps}\rangle ﹩, the max pooling layers are denoted as MAXPOOL–﹩\langle \mathrm{window}\,\mathrm{length}\rangle ﹩–﹩\langle \mathrm{stride}\,\mathrm{length}\rangle ﹩, and the fully connected layers are denoted as FC–﹩\langle \mathrm{number}\,\mathrm{of}\,\mathrm{units}\rangle ﹩. Right: the significantly reduced `Exonet-XS` model version described in Section 3.3.