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In this paper, we present list autoencoder (listAE) to mimic list decodingused in classical coding theory. With listAE, the decoder network outputs alist of decoded message word candidates. To train the listAE, a genie isassumed to be available at the output of the decoder. A specific loss functionis proposed to optimize the performance of a genie-aided (GA) list decoding.The listAE is a general framework and can be used with any AE architecture. Wepropose a specific architecture, referred to as incremental-redundancy AE(IR-AE), which decodes the received word on a sequence of component codes withnon-increasing rates. Then, the listAE is trained and evaluated with both IR-AEand Turbo-AE. Finally, we employ cyclic redundancy check (CRC) codes to replacethe genie at the decoder output and obtain a CRC aided (CA) list decoder. Oursimulation results show that the IR-AE under CA list decoding demonstratesmeaningful coding gain over Turbo-AE and polar code at low block error ratesrange.

List Autoencoder: Towards Deep Learning Based Reliable Transmission Over Noisy Channels