Title: Lmser-pix2seq: Learning Stable Sketch Representations For Sketch Healing. Abstract: Sketch healing aims to recreate a complete sketch from the corrupted one. The sparse and abstract nature of the sketch makes it challenging due to the difficulty in learning. The features extracted from the corrupted sketch may be inconsistent with the ones from the corresponding full sketch. In this paper, we present Lmser-pix2seq to learn stable sketch representations against the missing information by employing a Least mean square error reconstruction (Lmser) block, which falls into encoder-decoder paradigm. Taking as input a corrupted sketch, the Lmser encoder computes the embeddings of structural patterns of the input, while the decoder reconstructs the complete sketch from the embeddings. We build bi-directional skip connections between the encoder and the decoder in our Lmser block. The feedback connections enable recurrent paths to receive more information about the reconstructed sketch produced by the decoder, which helps the encoder extract stable sketch features. The features captured by the Lmser block are eventually fed into a recurrent neural network decoder to recreate the sketches. Experimental results show that our Lmser-pix2seq outperforms the state-of-the-art methods in sketch healing, especially when the sketches are heavily masked or corrupted.