Manning. After training, by making the speaker a different speaker from a speaker of the input audio data, i.e., by modifying the speaker latent embedding vector to a different vector from the speaker codebook, the decoder system 150 can effectively perform speaker conversion, i.e., transferring the voice from one speaker to another without changing the contents of what is said. 11 0 obj The discretisation of the embedded latent space has yielded significant performance gains across a variety of generative models. As noted in the introduction, we consider both reembedding these latents from scratch, or using the embeddings learned by the encoder. VQ-VAEs are typically learned by maximizing the ELBO assuming degenerate approximate posteriors as above, plus two terms that encourage the encoder embeddings and the code book embeddings to become close. As will be described in more detail below, the latent representation 122 includes a numeric representation that identifies features of the input audio data 102 in a latent space. The method may further comprise selecting, from a plurality of current content latent embedding vectors currently stored in the memory, a current content latent embedding vector that is nearest to the training speaker vector, and generating a training decoder input that includes the nearest current content latent embedding vectors and the nearest current speaker latent embedding vector. We The system then selects, for each latent variable and from the current content latent embedding vectors, a current content latent embedding vector that is nearest to the training encoded vector for the latent variable. The KL term vanishes to 0 and the qml distributions converge to the uniform priors. IB and 2. reconstruction conditioned on the decoder input. The modification of GAN architectures to include a discretised latent space in the descriminator has yielded state of the art performance across tasks including BigGAN for image generation, StyleGAN and StyleGAN2 for face generation, and unsupervised image-to-image translation. In these experiments we use each document in the development set of the AG News corpus as a query to retrieve 100 nearest neighbors in the training corpus, as measured by Hamming distance. The computer storage medium can be a machine-readable storage device, a machine-readable storage substrate, a random or serial access memory device, or a combination of one or more of them. These results are in line with those of Figure2, where VQ-VAE struggles when its code book vectors cannot be used (i.e., when reembedding from scratch). 1, appropriately programmed, can perform the process 400. In some cases, the subsystem 120 considers the entire set of content latent embedding vectors as possibilities for each of the content latent variables, i.e., selects the content latent embedding vector for each latent variable from the entire content codebook. To evaluatethe efciency of the latent representation in low-resource settings, we train the . Each latent in the former model corresponds to a word, and so we refer to this as a local model, 2013. The datasets we use for classification are AG News, DBPedia, and Yelp Review Full(Zhang etal., 2015), which correspond to predicting news labels, Wikipedia ontology labels, and the number of Yelp stars, respectively. 2019. In Figure3, we compare the average accuracy of our local and global model variants trained on 200 labeled examples, as we vary M. Improving Discrete Latent Representations With Differentiable Approximation Bridges Abstract:Modern neural network training relies on piece-wise (sub-)differentiable functions in order to use backpropagation to update model parameters. When reembedding global representations, performance increases as M does. The previous diagram depicts some of the fundamental elements of the VQGAN+CLIP architecture. There is still a vast scope for further investigating the advantages of discrete latent spaces, such as in the generator network of GANs, in other generative methods, and in more sophisticated architectures like VQ-VAE2. That is, the set of latent variables only need to be sent from the encoder system 100 to the decoder system 150 once in order for the decoder system 150 to be able to reconstruct audio data. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). HyAR constructs the latent space and embeds the dependence between discrete action and continuous parameter via an embedding table and conditional Variantional Auto-Encoder (VAE). For example, for a given embedding vector e,. FIG. , DEEPMIND TECHNOLOGIES LIMITED, UNITED KINGDOM, ASSIGNMENT OF ASSIGNORS INTEREST;ASSIGNORS:KAVUKCUOGLU, KORAY;VAN DEN OORD, AARON GERARD ANTONIUS;VINYALS, ORIOL;SIGNING DATES FROM 20180817 TO 20180822;REEL/FRAME:051955/0295, DOCKETED NEW CASE - READY FOR EXAMINATION, Method and apparatus for generating simulation scene, Audio conversion learning device, audio conversion device, method, and program, Speech coding using discrete latent representations, Content adaptive optimization for neural data compression, Synthetic generation of radar, LIDAR and ultrasonic measurement data, Latent representation scrambler and generative network adjustments, Identification value generation method for data increment step of microservice cluster, Training a model using parameter server shards, Neural architecture search using a performance prediction neural network, Predictive model training on large datasets, Neural architecture search for dense image prediction tasks, Semantically-consistent image style transfer, Speech coding using content latent embedding vectors and speaker latent embedding vectors, Scheduling computation graphs using neural networks, Regularized neural network architecture search, Population based training of neural networks, Training neural networks using priority queues, Training neural networks using consistency measures, Training machine learning models using teacher annealing, Computing method with dynamic minibatch sizes and computing system and computer-readable storage media for performing the same, Neural networks for scalable continual learning in domains with sequentially learned tasks, Energy-based associative memory neural networks, Data bank service update method, apparatus and system, Information on status: patent application and granting procedure in general. Estimating or propagating The input audio data 102 will generally be recorded or streaming speech, i.e., audio of one or more people speaking in a natural language. Discrete latent spaces compress the information bottleneck and enforce a regularisation upon the latent space. Tiancheng Zhao, Kyusong Lee, and Maxine Eskenazi. For the local model, let The discretised embedding is then converted back to a continuous representation and mapped to a prediction about whether the input image was real or fake. Our best classification models are able to outperform previous work, and this remains so even when we reembed discrete latents from scratch in the learned classifier. Both the discrete latent space and its uncertainty estimation are jointly learned during training. Moreover, the separation of various system modules and components in the embodiments described above should not be understood as requiring such separation in all embodiments, and it should be understood that the described program components and systems can generally be integrated together in a single software product or packaged into multiple software products. The system generates a discrete latent representation of the input audio data using the encoder output. Lukasz Kaiser, Samy Bengio, Aurko Roy, Ashish Vaswani, Niki Parmar, Jakob The system of any preceding claim, wherein the input audio data is a portion of an utterance, wherein the input audio data is preceded in the utterance by one or more other portions, and wherein generating the speaker vector comprises: applying mean pooling over the encoder vectors for the input audio data and encoder vectors generated for the one or more other portions of the utterance. Both the discrete latent space and its uncertainty estimation are jointly learned during training. Furthermore, we see that the Categorical VAE and VQ-VAE are largely comparable on average, though we undertake a finer-grained comparison by dataset in reparameterization with Gumbel-Softmax. 1A shows an example encoder system and an example decoder system. At the same time, deep generative models with discrete latent variables are attractive because the latents are arguably more interpretable, and because they lead to significantly more compressed representations: A representation consisting of M floating point values conventionally requires M32 bits, whereas M integers in {1,,K} requires only Mlog2K bits. and posterior collapse in variational autoencoders. The discrete latent representation can identify a nearest content latent embedding vector in any of a variety of ways. 16. The posterior categorical distribution q(zjx) probabilities are dened as one-hot as follows: q(z= kjx) = 1 for k = argmin jkz e(x) e jk 2, 0 . When the encoder system 100 and decoder system 150 are implemented on the same set of computers, the memory 130 and the memory 152 can be the same memory. 2018. Neural discrete representation learning. Joulin. In other implementations, the subsystem 120 is performing online audio coding and the current input audio is a portion of a larger utterance, i.e., the most recently received portion of the larger utterance. Built upon these representative discrete codes obtained from the entire target video, the subsequent discrete latent transformer is capable to infer proper codes for unknown areas under a self-attention mechanism, and thus produces fine-grained content with long-term spatial-temporal consistency. Discrete Latent spaces in deep generative models. northwest career and technical academy calendar; wonders grade 3 scope and sequence. by controlling growth of a volume of the embedding space. 2019. 4. For example, the actions recited in the claims can be performed in a different order and still achieve desirable results. It is also worth noting that storing a d-dimensional floating point representation of a sentence (as continuous latent variable approaches might) costs 32d bits, which is typically much larger. We tune other hyperparameters with random search and select the best settings based on validation accuracy. Learn on the go with our new app. Methods, systems, and apparatus, including computer programs encoded on computer storage media, for generating discrete latent representations of input data items. <> The plot above demonstrates how increasing the size of the codebook in the discrete latent space (as the dictionary size increases, the latent representation becomes closer to the continuous one) affects the performance of the network in the image synthesis task. The method of claim 9, wherein updating the current content latent embedding vectors and the current speaker latent embedding vectors comprises: for each latent variable, determining an update to the nearest current content latent embedding vector for the latent variable by determining a gradient with respect to the nearest current latent embedding vector to minimize an error between the training encoded vector for the latent variable and the nearest current content latent embedding vector to the training encoded vector for the latent variable. Here, the data distribution is encoded by the minimal representation in the computational flow, the latent space. The system generates the reconstruction of the input audio data by processing the decoder input using the decoder neural network (step 306). In case(1), interconnection topology using a combination of multiple neural nets, Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. 1997. DiederikP. Kingma, Tim Salimans, Rafal Jozefowicz, XiChen, Ilya Sutskever, The apparatus can also be, or further include, special purpose logic circuitry, e.g., an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit). 4 0 obj In the next subsection we quantitatively evaluate our discrete representations in a nearest neighbor-based retrieval setting. We compare the Wei Dong, Qinliang Su, Dinghan Shen, and Changyou Chen. Concretely, we compare different discrete latent variable models in following steps: Pretraining an encoder-decoder model on in-domain unlabeled text with an ELBO objective, with early stopping based on validation perplexity. 2014. FIG. The latent space has a particularly profound effect during inference, where both GANs and VAEs are used to generate images by sampling the latent space, then running these samples through the generator and decoder blocks respectively. Fixing the encoder to get discrete latents for the downstream classification task, and training a small number of task-specific parameters on top, using varying amounts of labeled data. Note, however, that we do not sample from the resulting categorical distributions. Discrete representations are potentially a more natural fit for many modalities, such as speech-related tasks. Determining the gradient with respect to the current values of the encoder network parameters may comprises copying gradients from the decoder input to the encoder output without updating the current speaker latent embedding vectors or current content latent embedding vectors. This resulting sequence of statistical machine translation: Parameter estimation, Smaller text In implementations the decoder neural network is an auto-regressive. 1993. We formulate the joint latent space as a combination of a continuous and a discrete latent factors in multi-modal data settings. Jeffrey Pennington, Richard Socher, and Christopher Manning. and obtain enc(x)m1 by taking the mth ~d-length subvector of the resulting pooled vector. While the above holds for storage, the space required to classify a sentence represented as ML integers using a parametric classifier may not be smaller than that required for classifying a sentence represented as a d-dimensional floating point vector. Only the discrete latent representation needs to be transmitted from an encoder system to a decoder system in order for the decoder system to be able to effectively to decode, i.e., reconstruct, the input audio data. AppendixA. : ge(y)i = {1if f e(y)i > 0.5 0otherwise, (5) with the discrete latent code zd (y) corresponding to 1 logK(g(y)). First, a network maps from the image space, containing real and fake images, to a continuous latent space. The system generates a decoder input from the discrete latent representation using the latent embedding vectors (step 304). The discrete latent representation can also identify the nearest speaker latent embedding vector using the same technique as is used to identify the nearest content latent embedding vectors. VQ-VAE and VQ-VAE2, which uses a heirachy of vector quanisations, now significantly outperform standard VAE architectures across an array of downstream generative tasks. corresponding to a second position in the sequence the representation identifies the content latent embedding vector e\, and so on. The method may further comprise generating a speaker vector, i.e. A program may, but need not, correspond to a file in a file system. Other features, aspects, and advantages of the subject matter will become apparent from the description, the drawings, and the claims. approach for disentangling syntax and semantics in sentence representations, Lexical heads, In these implementations, the discrete representation is being used to reduce the bandwidth required to transmit the input audio data 102 over the data communication network. The scores are averages over five random subsamples, with standard deviations in parentheses and column bests in. electromagnetic signal, that is generated to encode information for transmission to suitable receiver apparatus for execution by a data processing apparatus. In certain circumstances, multitasking and parallel processing may be advantageous. in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis, Speech or audio signals analysis-synthesis techniques for redundancy reduction, e.g. a set of speaker latent embedding vectors; one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to implement: process the input audio data to generate an encoder output that comprises a respective encoded vector corresponding to each latent variable m a sequence of latent variables; and, provide the input audio data as input to the encoder neural network to obtain the encoder output for the input audio data; and. We Love podcasts or audiobooks? Only the discrete latent representation needs to be transmitted from an encoder system to a decoder system in order for the decoder system to be able to effectively to decode, i.e., reconstruct, the input audio data. In the global model, we use the M embeddings directly. In both cases, the resulting sequence of discrete vectors is embedded and consumed by the decoder. For one or more computer programs to be configured to perform particular operations or actions means that the one or more programs include instructions that, when executed by data processing apparatus, cause the apparatus to perform the operations or actions. This paper presents a comparison of two different approaches which are broadly based on predicting future time-steps or auto-encoding the input signal. The system of claim 1, wherein the discrete latent representation of the input audio data includes (i) for each of the latent variables, an identifier of the nearest latent embedding vector to the encoded vector for the latent variable and (ii) an identifier of the speaker latent embedding vector that is nearest to the speaker vector and/or preferably wherein the encoder neural network is convolutional neural network. Unsurprisingly, when not reembedding, M matters less. IB, the encoder output 112 is a sequence of D dimensional vectors, with each position in the sequence corresponding to a respective latent variable. In some implementations, the encoder system 100 and the decoder system 150 are implemented on the same set of one or more computers, e.g., when the discrete To provide for interaction with a user, embodiments of the subject matter described in this specification can be implemented on a computer having a display device, e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor, for displaying information to the user and a keyboard and a pointing device, e.g., a mouse or a trackball, by which the user can provide input to the computer. We pretrain VAMPIRE models on in-domain text for each dataset with 60 random hyperparameter search (with same ranges as specified in their Appendix A.1), and select best models based on validation accuracy in each setting. , Toyota Technological Institute at Chicago, Truncated Inference for Latent Variable Optimization Problems: The latent space is ideally a minimal representation of the semantic and spatial information found in the data distribution. We describe latent Dirichlet allocation (LDA), a generative probabilistic model for collections of discrete data such as text corpora. For instance, the sentence (from the DBPedia dataset) backlash is a 1986 australian film directed by bill bennett is encoded as Here, a lower FID score means a superior image quality and thus we can conclude that increasing the size of the codebook degrades the performance of the network. Also or instead updating the current content latent embedding vectors and the current speaker latent embedding vectors may include determining a respective commitment update to the current values of the encoder parameters by determining a gradient with respect to the current values of the encoder parameters to minimize a commitment loss, between the training encoded vector for the latent variable and the nearest current content latent embedding vector to the training encoded vector for the latent variable. A standard Categorical VAE Triangular and circular markers correspond to global and local models, respectively. Originals and reconstructions Samples from Prior The discrete latent space captures the important aspects of the audio, such as the content of the speech, in a very compressed symbolic representation. The system processes the input audio data using an encoder neural network to generate an encoder output for the input audio data (step 204). As another particular example, the subsystem 120 can generate the speaker vector by performing mean pooling over the vectors in (i) the current encoder output and (ii) all previous encoder outputs for previously received portions of the larger utterance that are within a threshold time window of the current audio input in the larger utterance. summary of key features), which can be used to reconstruct the output. 1, appropriately programmed, can perform the process 200. The decoder input 162 includes, for each latent variable, the content latent embedding vector that is identified for the latent variable in the discrete latent representation 122. The system of any preceding claim, wherein generating the speaker vector comprises: applying mean pooling over the encoder vectors. 12. We show that our discrete representations outperform these previous results while being significantly more lightweight. For the global model, we obtain the parameters of each categorical approximate posterior qm1 as in vocoders; Coding or decoding of speech or audio signals, using source filter models or psychoacoustic analysis using predictive techniques, COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS, Computing arrangements based on biological models, Computing arrangements based on biological models using neural network models, Architectures, e.g. 2019. stream One or more computer storage media storing the instructions of any one of claims 1-8. (2019) and other baselines; determining a pitch reconstruction update to the current values of the pitch reconstruction network parameters and the encoder network parameters by determining a gradient with respect to the current values of the pitch reconstruction network parameters and the encoder network parameters to optimize a reconstruction error between the training reconstruction of the pitch track and a ground truth pitch track of the training audio input. AidanN Gomez, ukasz Kaiser, and Illia Polosukhin. First we define a relaxed version of z, ~z, where each We see that CatVAE and Hard EM outperform these CBOW baselines (while being significantly more space efficient), while VQ-VAE does not. To demonstrate the usefulness of our models, we focus on improving low-resource classification performance by pretraining on unlabeled text. For argument representation learning, different from previous methods focusing on the modeling of continuous argument representations, we obtain discrete latent representations via discrete varia-tional autoencoders and investigate their effects on the understanding of dialogical argumentative structure. exhibit similar patterns, we focus on the latter model. That is, we have p(x,z;)=p(z)T t=1p(xt|x<t,z;), where are the generative model's parameters. the decoder input to generate a reconstruction of the input audio data. Van den Oord et al., NeurIPS, 2017. the best combinations of model and hyperparameters when training with 200 labeled examples on AG News. In some other implementations, the system can update the current content latent embedding vectors as a function of the moving averages of the encoded vectors in the training encoder outputs. LDA is a three-level hierarchical Bayesian model, in which each item of a collection is modeled as a finite mixture over an underlying set of topics. ChrisJ. Maddison, Andriy Mnih, and YeeWhye Teh. The system determines updates to the current latent embedding vectors that are stored in the memory (step 408). That is, for each current content latent embedding vector, the system can update the embedding vector using exponential moving averages of the n encoded vectors that are nearest to the embedding vector. The encoder (parameterized by ) maps an example x to the parameters of an approximate posterior distribution. Interestingly, we find that an amortized variant of Hard EM performs 2014. The output of each hidden layer is used as input to the next layer in the network, i.e., the next hidden layer or the output layer. The commitment loss may be configured to help the encoder neural averaged over the development datasets of AG News, DBPedia, and Yelp Full. FIG. The termdata processing apparatus refers to data processing hardware and encompasses all kinds of apparatus, devices, and machines for processing data, including Each topic is Benefit from the discrete nature of the latent representations, MUE can estimate any input the conditional probability distribution of the outputs effectively. ~zml is a softmax over K outputs (rather than a hard assignment) and is produced by an inference network with parameters .222Note this assumes our generative model can condition on such a relaxed latent variable. The pitch track may be a measure of a pitch of the training audio input e.g. In order to learn a discrete latent representation, we incorporate ideas from vector quantisation (VQ). The method of any one of claims 15-16, wherein determining the gradient with respect to the current values of the encoder network parameters to optimize a reconstruction error between the training reconstruction of the pitch track and a ground truth pitch track of the training audio input comprises: 19. Conversely, various features that are described in The subsystem may provide the decoder input as input to the decoder neural network to obtain the reconstruction of the input audio data. In this paper, we propose Hybrid Action Representation (HyAR) to learn a compact and decodable latent representation space for the original hybrid action space. particularly well in the lowest-resource regimes. with mixture-prior generative models, Variational pretraining 9 0 obj Since in this case KL(qml(zml|x)||pml(zml))=logKH[qml(zml|x)], this is equivalent to thresholding H[qml(zml|x)] by (1)logK. 18. a VQ-VAE, and Hard EM in terms of their ability to improve a low-resource text classification system, and to allow for nearest neighbor-based document retrieval. our discrete representations by training a small classifier on top of them. 15 0 obj One or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to classification and generation, Extractive Summary as Discrete Latent Variables, Learning Dependencies of Discrete Speech Representations with Neural When the encoder system 100 and the decoder system 150 are remote from one another, the encoder system 100 can send the decoder system 150 the latent embedding vectors that are stored in the memory 130 prior to the decoder system 150 being used to reconstruct audio data. For convenience, the process 200 will be described as being performed by a system of one or more computers located in one or more locations. We therefore maximize: where q(z|x;)=Mm=1Ll=1qml(zml|x;), pml=1/K, and H is the entropy. We use GloVe with a 2.2 million vocabulary (Pennington etal., 2014) and fastText with a 2 million vocabulary (Mikolov etal., 2018). 21. endobj Xiang Zhang, Junbo Zhao, and Yann LeCun. In implementations generating the speaker vector from at least the encoded vectors in the encoder output uses multiple encoded vectors to generate the speaker vector. variables, discrete latent variables are interesting because they are more Our ndings show that continuous relax-ation training of discrete latent variable models is a powerful method for learning representations that can exibly capture both continuous and discrete aspects of natural data.
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