本文介绍了一个有效的基于补丁的计算模块，基于熵的补丁编码器（EPE）模块，用于资源受限的语义分割。 EPE模块由三个轻巧的全趋验编码器组成，每个编码器都会从图像贴片中提取特征，并具有不同量的熵。编码器的参数数量最多，带有中等熵的贴片由具有中等数量的参数处理，并且具有适度的参数的编码器正在处理高熵的补丁，并且最小的编码器处理了低熵的贴片。模块背后的直觉是：由于具有高熵的补丁包含更多信息，因此它们需要具有更多参数的编码器，与低熵补丁不同，可以使用小编码器处理。因此，通过较小的编码器处理部分可以显着降低模块的计算成本。实验表明，EPE可以提高现有的实时语义分割模型的性能，并略有增加计算成本。具体而言，EPE将DFANET A的MIOU性能提高了0.9％，而参数数量仅增加1.2％，而Edanet的MIOU性能则增加了1％，而模型参数增加了10％。

We discover a robust self-supervised strategy tailored towards molecular representations for generative masked language models through a series of tailored, in-depth ablations. Using this pre-training strategy, we train BARTSmiles, a BART-like model with an order of magnitude more compute than previous self-supervised molecular representations. In-depth evaluations show that BARTSmiles consistently outperforms other self-supervised representations across classification, regression, and generation tasks setting a new state-of-the-art on 11 tasks. We then quantitatively show that when applied to the molecular domain, the BART objective learns representations that implicitly encode our downstream tasks of interest. For example, by selecting seven neurons from a frozen BARTSmiles, we can obtain a model having performance within two percentage points of the full fine-tuned model on task Clintox. Lastly, we show that standard attribution interpretability methods, when applied to BARTSmiles, highlight certain substructures that chemists use to explain specific properties of molecules. The code and the pretrained model are publicly available.