Despite the advances achieved by neural models in sequence to sequence learning, exploited in a variety of tasks, they still make errors. In many use cases, these are corrected by a human expert in a posterior revisio...
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The term fine-grained visual classification (FGVC) refers to classification tasks where the classes are very similar and the classification model needs to be able to find subtle differences to make the correct predict...
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Sparse models require less memory for storage and enable a faster inference by reducing the necessary number of FLOPs. This is relevant both for time-critical and on-device computations using neural networks. The stab...
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This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs,...
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This work addresses the problem of automatically segmenting the MR images corresponding to the lumbar spine. The purpose is to detect and delimit the different structural elements like vertebrae, intervertebral discs, nerves, blood vessels, etc. This task is known as semantic segmentation. The approach proposed in this work is based on convolutional neural networks whose output is a mask where each pixel from the input image is classified into one of the possible classes. Classes were defined by radiologists and correspond to structural elements and tissues. The proposed network architectures are variants of the U-Net. Several complementary blocks were used to define the variants: spatial attention models, deep supervision and multi-kernels at input, this last block type is based on the idea of inception. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline. Those architectures which got the best results are described in this paper, and their results are discussed. Two of the proposed architectures outperform the standard U-Net used as baseline.
To join the advantages of classical and end-to-end approaches for speech recognition, we present a simple, novel and competitive approach for phoneme-based neural transducer modeling. Different alignment label topolog...
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In hybrid HMM based speech recognition, LSTM language models have been widely applied and achieved large improvements. The theoretical capability of modeling any unlimited context suggests that no recombination should...
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Sequence-to-sequence models with an implicit alignment mechanism (e.g. attention) are closing the performance gap towards traditional hybrid hidden Markov models (HMM) for the task of automatic speech recognition. One...
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The RNN transducer is a promising end-to-end model candidate. We compare the original training criterion with the full marginalization over all alignments, to the commonly used maximum approximation, which simplifies,...
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We present a complete training pipeline to build a state-of-the-art hybrid HMM-based ASR system on the 2nd release of the TED-LIUM corpus. Data augmentation using SpecAugment is successfully applied to improve perform...
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As one popular modeling approach for end-to-end speech recognition, attention-based encoder-decoder models are known to suffer the length bias and corresponding beam problem. Different approaches have been applied in ...
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