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Generative feature matching network (GFMN) is an approach for training implicit generative models for images by performing moment matching on features from pre-trained neural networks. In this paper, we present new GFMN formulations that are effective for sequential data. Our experimental results show the effectiveness of the proposed method, SeqGFMN, for three distinct generation tasks in English: unconditional text generation, class-conditional text generation, and unsupervised text style transfer. SeqGFMN is stable to train and outperforms various adversarial approaches for text generation and text style transfer., Comment: ACL 2020

Tabular datasets are ubiquitous in data science applications. Given their importance, it seems natural to apply state-of-the-art deep learning algorithms in order to fully unlock their potential. Here we propose neural network models that represent tabular time series that can optionally leverage their hierarchical structure. This results in two architectures for tabular time series: one for learning representations that is analogous to BERT and can be pre-trained end-to-end and used in downstream tasks, and one that is akin to GPT and can be used for generation of realistic synthetic tabular sequences. We demonstrate our models on two datasets: a synthetic credit card transaction dataset, where the learned representations are used for fraud detection and synthetic data generation, and on a real pollution dataset, where the learned encodings are used to predict atmospheric pollutant concentrations. Code and data are available at https://github.com/IBM/TabFormer., Comment: Accepted to ICASSP, 2021; https://github.com/IBM/TabFormer

Perceptual features (PFs) have been used with great success in tasks such as transfer learning, style transfer, and super-resolution. However, the efficacy of PFs as key source of information for learning generative models is not well studied. We investigate here the use of PFs in the context of learning implicit generative models through moment matching (MM). More specifically, we propose a new effective MM approach that learns implicit generative models by performing mean and covariance matching of features extracted from pretrained ConvNets. Our proposed approach improves upon existing MM methods by: (1) breaking away from the problematic min/max game of adversarial learning; (2) avoiding online learning of kernel functions; and (3) being efficient with respect to both number of used moments and required minibatch size. Our experimental results demonstrate that, due to the expressiveness of PFs from pretrained deep ConvNets, our method achieves state-of-the-art results for challenging benchmarks., Comment: 16 pages

In this paper we study image captioning as a conditional GAN training, proposing both a context-aware LSTM captioner and co-attentive discriminator, which enforces semantic alignment between images and captions. We empirically focus on the viability of two training methods: Self-critical Sequence Training (SCST) and Gumbel Straight-Through (ST) and demonstrate that SCST shows more stable gradient behavior and improved results over Gumbel ST, even without accessing discriminator gradients directly. We also address the problem of automatic evaluation for captioning models and introduce a new semantic score, and show its correlation to human judgement. As an evaluation paradigm, we argue that an important criterion for a captioner is the ability to generalize to compositions of objects that do not usually co-occur together. To this end, we introduce a small captioned Out of Context (OOC) test set. The OOC set, combined with our semantic score, are the proposed new diagnosis tools for the captioning community. When evaluated on OOC and MS-COCO benchmarks, we show that SCST-based training has a strong performance in both semantic score and human evaluation, promising to be a valuable new approach for efficient discrete GAN training., Authors Equal Contribution, CVPR 2019

This paper proposes an acoustic modeling approach based on bootstrap and restructuring to dealing with data sparsity for low-resourced languages. The goal of the approach is to improve the statistical reliability of acoustic modeling for automatic speech recognition (ASR) in the context of speed, memory and response latency requirements for real-world applications. In this approach, randomized hidden Markov models (HMMs) estimated from the bootstrapped training data are aggregated for reliable sequence prediction. The aggregation leads to an HMM with superior prediction capability at cost of a substantially larger size. For practical usage the aggregated HMM is restructured by Gaussian clustering followed by model refinement. The restructuring aims at reducing the aggregated HMM to a desirable model size while maintaining its performance close to the original aggregated HMM. To that end, various Gaussian clustering criteria and model refinement algorithms have been investigated in the full covariance model space before the conversion to the diagonal covariance model space in the last stage of the restructuring. Large vocabulary continuous speech recognition (LVCSR) experiments on Pashto and Dari have shown that acoustic models obtained by the proposed approach can yield superior performance over the conventional training procedure with almost the same run-time memory consumption and decoding speed.

Deep Scattering Network features introduced for image processing have recently proved useful in speech recognition as an alternative to log-mel features for Deep Neural Network (DNN) acoustic models. Scattering features use wavelet decomposition directly producing log-frequency spectrograms which are robust to local time warping and provide additional information within higher order coefficients. This paper extends previous works by showing how scattering features perform on a state-of-the-art spontaneous speech recognition utilizing DNN acoustic model. We revisit feature normalization and compression topics in an extensive study, putting emphasis on comparing models of the same size. We observe that scattering features outperform baseline log-mel in all conditions, with additional gains from multi-resolution processing.

A significant barrier to progress in automatic speech recognition (ASR) capability is the empirical reality that techniques rarely “scale”—the yield of many apparently fruitful techniques rapidly diminishes to zero as the training criterion or decoder is strengthened, or the size of the training set is increased. Recently we showed that annealed dropout—a regularization procedure which gradually reduces the percentage of neurons that are randomly zeroed out during DNN training—leads to substantial word error rate reductions in the case of small to moderate training data amounts, and acoustic models trained based on the cross-entropy (CE) criterion [1]. In this paper we show that deep Maxout networks trained using annealed dropout can substantially improve the quality of commercial-grade LVCSR systems even when the acoustic model is trained with sequence-level training criterion, and on large amounts of data.