Supervised Encoding for Discrete Representation Learning
Classical supervised classification tasks search for a nonlinear mapping that maps each encoded feature directly to a probability mass over the labels. Such a learning framework typically lacks the intuition that encoded features from the same class tend to be similar and thus has little interpretability for the learned features. In this paper, we propose a novel supervised learning model named Supervised-Encoding Quantizer (SEQ). The SEQ applies a quantizer to cluster and classify the encoded features. We found that the quantizer provides an interpretable graph where each cluster in the graph represents a class of data samples that have a particular style. We also trained a decoder that can decode convex combinations of the encoded features from similar and different clusters and provide guidance on style transfer between sub-classes.
Code (1)
Tasks
DecoderRepresentation LearningStyle TransferMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Self-Supervised Learning as Discrete Communication
Most self-supervised learning (SSL) methods learn continuous visual representations by aligning different views of the same input, offering limited control over how information is structured across representation dimensi…
Self-Supervised LearningImage ClassificationDiscrete Codebook World Models for Continuous Control
In reinforcement learning (RL), world models serve as internal simulators, enabling agents to predict environment dynamics and future outcomes in order to make informed decisions. While previous approaches leveraging dis…
continuous-controlContinuous ControlModel Predictive ControlReinforcement Learning (RL)Benchmarking Prosody Encoding in Discrete Speech Tokens
Recently, discrete tokens derived from self-supervised learning (SSL) models via k-means clustering have been actively studied as pseudo-text in speech language models and as efficient intermediate representations for va…
Self-Supervised LearningUnsupervised speech representation learning using WaveNet autoencoders
We consider the task of unsupervised extraction of meaningful latent representations of speech by applying autoencoding neural networks to speech waveforms. The goal is to learn a representation able to capture high leve…
Acoustic Unit DiscoveryDecoderDimensionality ReductionRepresentation Learning+1Discrete Facial Encoding: : A Framework for Data-driven Facial Display Discovery
Facial expression analysis is central to understanding human behavior, yet existing coding systems such as the Facial Action Coding System (FACS) are constrained by limited coverage and costly manual annotation. In this …
Representation Learning