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Found 13 papers, 8 papers with code
Task-Equivariant Graph Few-shot Learning
To solve this problem, it is important for GNNs to be able to classify nodes with a limited number of labeled nodes, known as few-shot node classification.
Conditional Graph Information Bottleneck for Molecular Relational Learning
Molecular relational learning, whose goal is to learn the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications.
Predicting Density of States via Multi-modal Transformer
The density of states (DOS) is a spectral property of materials, which provides fundamental insights on various characteristics of materials.
Guided-TTS 2: A Diffusion Model for High-quality Adaptive Text-to-Speech with Untranscribed Data
We train the speaker-conditional diffusion model on large-scale untranscribed datasets for a classifier-free guidance method and further fine-tune the diffusion model on the reference speech of the target speaker for adaptation, which only takes 40 seconds.
Perception Prioritized Training of Diffusion Models
Diffusion models learn to restore noisy data, which is corrupted with different levels of noise, by optimizing the weighted sum of the corresponding loss terms, i. e., denoising score matching loss.
Guided-TTS: A Diffusion Model for Text-to-Speech via Classifier Guidance
For TTS synthesis, we guide the generative process of the diffusion model with a phoneme classifier trained on a large-scale speech recognition dataset.
FICGAN: Facial Identity Controllable GAN for De-identification
In this work, we present Facial Identity Controllable GAN (FICGAN) for not only generating high-quality de-identified face images with ensured privacy protection, but also detailed controllability on attribute preservation for enhanced data utility.
Guided-TTS:Text-to-Speech with Untranscribed Speech
By modeling the unconditional distribution for speech, our model can utilize the untranscribed data for training.
AligNART: Non-autoregressive Neural Machine Translation by Jointly Learning to Estimate Alignment and Translate
Non-autoregressive neural machine translation (NART) models suffer from the multi-modality problem which causes translation inconsistency such as token repetition.
ILVR: Conditioning Method for Denoising Diffusion Probabilistic Models
In this work, we propose Iterative Latent Variable Refinement (ILVR), a method to guide the generative process in DDPM to generate high-quality images based on a given reference image.
NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity
Normalizing flows (NFs) have become a prominent method for deep generative models that allow for an analytic probability density estimation and efficient synthesis.
Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search
By leveraging the properties of flows, MAS searches for the most probable monotonic alignment between text and the latent representation of speech.
Ranked #4 on
Text-To-Speech Synthesis
on LJSpeech
(using extra training data)
FloWaveNet : A Generative Flow for Raw Audio
Most of modern text-to-speech architectures use a WaveNet vocoder for synthesizing a high-fidelity waveform audio, but there has been a limitation for practical applications due to its slow autoregressive sampling scheme.
Sound Audio and Speech Processing
