{"list":"other","total":53,"rows":[{"id":"1412.6980","title":"Adam: A Method for Stochastic Optimization","created":"2014-12-22","cited_by_count":37240},{"id":"1505.04597","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","created":"2015-05-18","cited_by_count":19713},{"id":"1503.02531","title":"Distilling the Knowledge in a Neural Network","created":"2015-03-09","cited_by_count":10324},{"id":"1608.06993","title":"Densely Connected Convolutional Networks","created":"2016-08-25","cited_by_count":6720},{"id":"1607.06450","title":"Layer Normalization","created":"2016-07-21","cited_by_count":5669},{"id":"2006.05525","title":"Knowledge Distillation: A Survey","created":"2020-06-09","cited_by_count":1603},{"id":"1911.04252","title":"Self-training with Noisy Student improves ImageNet classification","created":"2019-11-11","cited_by_count":1311},{"id":"1603.06937","title":"Stacked Hourglass Networks for Human Pose Estimation","created":"2016-03-22","cited_by_count":915},{"id":"1612.08083","title":"Language Modeling with Gated Convolutional Networks","created":"2016-12-23","cited_by_count":702},{"id":"1908.02983","title":"Pseudo-Labeling and Confirmation Bias in Deep Semi-Supervised Learning","created":"2019-08-08","cited_by_count":510},{"id":"1809.07454","title":"Conv-TasNet: Surpassing Ideal Time-Frequency Magnitude Masking for Speech Separation","created":"2018-09-20","cited_by_count":406},{"id":"2003.10580","title":"Meta Pseudo Labels","created":"2020-03-23","cited_by_count":374},{"id":"1508.04306","title":"Deep clustering: Discriminative embeddings for segmentation and separation","created":"2015-08-18","cited_by_count":212},{"id":"1707.00183","title":"Teacher-Student Curriculum Learning","created":"2017-07-01","cited_by_count":210},{"id":"2006.12847","title":"Real Time Speech Enhancement in the Waveform Domain","created":"2020-06-23","cited_by_count":186},{"id":"2010.13154","title":"Attention is All You Need in Speech Separation","created":"2020-10-25","cited_by_count":185},{"id":"1910.06379","title":"Dual-path RNN: efficient long sequence modeling for time-domain single-channel speech separation","created":"2019-10-14","cited_by_count":182},{"id":"2006.07114","title":"Knowledge Distillation Meets Self-Supervision","created":"2020-06-12","cited_by_count":142},{"id":"1806.03185","title":"Wave-U-Net: A Multi-Scale Neural Network for End-to-End Audio Source Separation","created":"2018-06-08","cited_by_count":125},{"id":"1911.13254","title":"Music Source Separation in the Waveform Domain","created":"2019-11-27","cited_by_count":99},{"id":"2111.03600","title":"Hybrid Spectrogram and Waveform Source Separation","created":"2021-11-05","cited_by_count":91},{"id":"2007.13975","title":"Dual-Path Transformer Network: Direct Context-Aware Modeling for End-to-End Monaural Speech Separation","created":"2020-07-28","cited_by_count":68},{"id":"2010.15508","title":"FullSubNet: A Full-Band and Sub-Band Fusion Model for Real-Time Single-Channel Speech Enhancement","created":"2020-10-29","cited_by_count":68},{"id":"1909.01174","title":"Demucs: Deep Extractor for Music Sources with extra unlabeled data remixed","created":"2019-09-03","cited_by_count":54},{"id":"2109.13731","title":"VoiceFixer: Toward General Speech Restoration with Neural Vocoder","created":"2021-09-28","cited_by_count":50},{"id":"2109.05418","title":"Decoupling Magnitude and Phase Estimation with Deep ResUNet for Music Source Separation","created":"2021-09-12","cited_by_count":48},{"id":"1804.06267","title":"The 2018 Signal Separation Evaluation Campaign","created":"2018-04-17","cited_by_count":41},{"id":"2108.13559","title":"Music Demixing Challenge 2021","created":"2021-08-31","cited_by_count":38},{"id":"2104.13002","title":"DPT-FSNet: Dual-path Transformer Based Full-band and Sub-band Fusion Network for Speech Enhancement","created":"2021-04-27","cited_by_count":33},{"id":"1708.05466","title":"Large-Scale Domain Adaptation via Teacher-Student Learning","created":"2017-08-17","cited_by_count":31},{"id":"2203.12188","title":"FullSubNet+: Channel Attention FullSubNet with Complex Spectrograms for Speech Enhancement","created":"2022-03-23","cited_by_count":29},{"id":"2010.01733","title":"D3Net: Densely connected multidilated DenseNet for music source separation","created":"2020-10-05","cited_by_count":24},{"id":"2111.12203","title":"KUIELab-MDX-Net: A Two-Stream Neural Network for Music Demixing","created":"2021-11-24","cited_by_count":24},{"id":"1811.11307","title":"Improved Speech Enhancement with the Wave-U-Net","created":"2018-11-27","cited_by_count":23},{"id":"2010.06007","title":"The Cone of Silence: Speech Separation by Localization","created":"2020-10-12","cited_by_count":21},{"id":"1502.04149","title":"Joint Optimization of Masks and Deep Recurrent Neural Networks for Monaural Source Separation","created":"2015-02-13","cited_by_count":20},{"id":"2202.08862","title":"RemixIT: Continual self-training of speech enhancement models via bootstrapped remixing","created":"2022-02-17","cited_by_count":20},{"id":"1805.02410","title":"MMDenseLSTM: An efficient combination of convolutional and recurrent neural networks for audio source separation","created":"2018-05-07","cited_by_count":19},{"id":"2002.07016","title":"Meta-learning Extractors for Music Source Separation","created":"2020-02-17","cited_by_count":18},{"id":"2108.03456","title":"A Unified Model for Zero-shot Music Source Separation, Transcription and Synthesis","created":"2021-08-07","cited_by_count":18},{"id":"2111.08387","title":"S-DCCRN: Super Wide Band DCCRN with learnable complex feature for speech enhancement","created":"2021-11-16","cited_by_count":14},{"id":"2206.07347","title":"On the Use of Deep Mask Estimation Module for Neural Source Separation Systems","created":"2022-06-15","cited_by_count":11},{"id":"1611.06265","title":"Deep Clustering and Conventional Networks for Music Separation: Stronger Together","created":"2016-11-18","cited_by_count":10},{"id":"2106.07843","title":"Teacher-Student MixIT for Unsupervised and Semi-supervised Speech Separation","created":"2021-06-15","cited_by_count":9},{"id":"2112.04685","title":"CWS-PResUNet: Music Source Separation with Channel-wise Subband Phase-aware ResUNet","created":"2021-12-09","cited_by_count":9},{"id":"2011.08397","title":"Ultra-Lightweight Speech Separation via Group Communication","created":"2020-11-17","cited_by_count":8},{"id":"2012.07291","title":"Group Communication with Context Codec for Lightweight Source Separation","created":"2020-12-14","cited_by_count":7},{"id":"2110.10103","title":"Continual self-training with bootstrapped remixing for speech enhancement","created":"2021-10-19","cited_by_count":6},{"id":"2110.10757","title":"TPARN: Triple-path Attentive Recurrent Network for Time-domain Multichannel Speech Enhancement","created":"2021-10-20","cited_by_count":6},{"id":"2102.07961","title":"Semi-Supervised Singing Voice Separation with Noisy Self-Training","created":"2021-02-16","cited_by_count":5},{"id":"2006.13579","title":"Multi-path RNN for hierarchical modeling of long sequential data and its application to speaker stream separation","created":"2020-06-24","cited_by_count":2},{"id":"1805.08559","title":"Music Source Separation Using Stacked Hourglass Networks","created":"2018-05-22","cited_by_count":1},{"id":"2201.10809","title":"A two-step backward compatible fullband speech enhancement system","created":"2022-01-26","cited_by_count":1}],"next_cursor":null}