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Found 28 papers, 15 papers with code
Reduce, Reuse, Recycle: Compositional Generation with Energy-Based Diffusion Models and MCMC
In this work, we build upon these ideas using the score-based interpretation of diffusion models, and explore alternative ways to condition, modify, and reuse diffusion models for tasks involving compositional generation and guidance.
Continuous diffusion for categorical data
Diffusion models have quickly become the go-to paradigm for generative modelling of perceptual signals (such as images and sound) through iterative refinement.
Self-conditioned Embedding Diffusion for Text Generation
Can continuous diffusion models bring the same performance breakthrough on natural language they did for image generation?
Categorical SDEs with Simplex Diffusion
Diffusion models typically operate in the standard framework of generative modelling by producing continuously-valued datapoints.
General-purpose, long-context autoregressive modeling with Perceiver AR
Real-world data is high-dimensional: a book, image, or musical performance can easily contain hundreds of thousands of elements even after compression.
Variable-rate discrete representation learning
Semantically meaningful information content in perceptual signals is usually unevenly distributed.
Generating Images with Sparse Representations
The high dimensionality of images presents architecture and sampling-efficiency challenges for likelihood-based generative models.
A Deep Learning Approach for Characterizing Major Galaxy Mergers
Fine-grained estimation of galaxy merger stages from observations is a key problem useful for validation of our current theoretical understanding of galaxy formation.
Towards transformation-resilient provenance detection of digital media
In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations.
Self-Supervised MultiModal Versatile Networks
In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding.
End-to-End Adversarial Text-to-Speech
Modern text-to-speech synthesis pipelines typically involve multiple processing stages, each of which is designed or learnt independently from the rest.
Transformation-based Adversarial Video Prediction on Large-Scale Data
Recent breakthroughs in adversarial generative modeling have led to models capable of producing video samples of high quality, even on large and complex datasets of real-world video.
Ranked #7 on
Video Prediction
on Kinetics-600 12 frames, 64x64
Provenance detection through learning transformation-resilient watermarking
In this paper, we introduce ReSWAT (Resilient Signal Watermarking via Adversarial Training), a framework for learning transformation-resilient watermark detectors that are able to detect a watermark even after a signal has been through several post-processing transformations.
High Fidelity Speech Synthesis with Adversarial Networks
However, their application in the audio domain has received limited attention, and autoregressive models, such as WaveNet, remain the state of the art in generative modelling of audio signals such as human speech.
Hierarchical Autoregressive Image Models with Auxiliary Decoders
We show that autoregressive models conditioned on these representations can produce high-fidelity reconstructions of images, and that we can train autoregressive priors on these representations that produce samples with large-scale coherence.
Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset
Generating musical audio directly with neural networks is notoriously difficult because it requires coherently modeling structure at many different timescales.
Piano Genie
We present Piano Genie, an intelligent controller which allows non-musicians to improvise on the piano.
This Time with Feeling: Learning Expressive Musical Performance
Music generation has generally been focused on either creating scores or interpreting them.
The challenge of realistic music generation: modelling raw audio at scale
It has been shown that autoregressive models excel at generating raw audio waveforms of speech, but when applied to music, we find them biased towards capturing local signal structure at the expense of modelling long-range correlations.
Efficient Neural Audio Synthesis
The small number of weights in a Sparse WaveRNN makes it possible to sample high-fidelity audio on a mobile CPU in real time.
Parallel WaveNet: Fast High-Fidelity Speech Synthesis
The recently-developed WaveNet architecture is the current state of the art in realistic speech synthesis, consistently rated as more natural sounding for many different languages than any previous system.
Neural Audio Synthesis of Musical Notes with WaveNet Autoencoders
Generative models in vision have seen rapid progress due to algorithmic improvements and the availability of high-quality image datasets.
WaveNet: A Generative Model for Raw Audio
This paper introduces WaveNet, a deep neural network for generating raw audio waveforms.
Ranked #1 on
Speech Synthesis
on Mandarin Chinese
Theano: A Python framework for fast computation of mathematical expressions
Since its introduction, it has been one of the most used CPU and GPU mathematical compilers - especially in the machine learning community - and has shown steady performance improvements.
Exploiting Cyclic Symmetry in Convolutional Neural Networks
We evaluate the effect of these architectural modifications on three datasets which exhibit rotational symmetry and demonstrate improved performance with smaller models.
Beyond Temporal Pooling: Recurrence and Temporal Convolutions for Gesture Recognition in Video
Recent studies have demonstrated the power of recurrent neural networks for machine translation, image captioning and speech recognition.
Ranked #1 on
Gesture Recognition
on Montalbano
Rotation-invariant convolutional neural networks for galaxy morphology prediction
Unfortunately, even this approach does not scale well enough to keep up with the increasing availability of galaxy images.
Deep content-based music recommendation
We also show that recent advances in deep learning translate very well to the music recommendation setting, with deep convolutional neural networks significantly outperforming the traditional approach.
