15 accepted papers
Neural networks for continual reinforcement learning (CRL) often suffer from plasticity loss, i.e., a progressive decline in their ability to learn new tasks arising from increased representational drift (churn) and Neural Tangent Kernel (NTK) rank collapse. Current methods mitigating this problem i…
Predictive Coding Networks (PCNs) offer a biologically inspired alternative to conventional deep neural networks. However, their scalability is hindered by severe training instabilities that intensify with network depth. Through dynamical mean-field analyses, we identify two fundamental pathologies…
Diffusion distillation models effectively accelerate reverse sampling by compressing the process into fewer steps. However, these models still exhibit a performance gap compared to their pre-trained diffusion model counterparts, exacerbated by distribution shifts and accumulated errors during multi-…
Diffusion models have significantly facilitated the customization of input video with target appearance while maintaining its motion patterns. To distill the motion information from video frames, existing works often estimate motion representations as frame difference or correlation in pixel-/featur…
The brain prepares for learning even before interacting with the environment, by refining and optimizing its structures through spontaneous neural activity that resembles random noise. However, the mechanism of such a process has yet to be understood, and it is unclear whether this process can benef…
Spurious correlations can cause strong biases in deep neural networks impairing generalization ability. While most existing debiasing methods require full supervision on either spurious attributes or target labels training a debiased model from a limited amount of both annotations is still an open q…
Despite the remarkable performance of text-to-image diffusion models in image generation tasks, recent studies have raised the issue that generated images sometimes cannot capture the intended semantic contents of the text prompts, which phenomenon is often called semantic misalignment. To address t…
Neural networks are often biased to spuriously correlated features that provide misleading statistical evidence that does not generalize. This raises an interesting question: "Does an optimal unbiased functional subnetwork exist in a severely biased network? If so, how to extract such subnetwork?" W…
Human skeleton-based action recognition offers a valuable means to understand the intricacies of human behavior because it can handle the complex relationships between physical constraints and intention. Although several studies have focused on encoding a skeleton, less attention has been paid to em…
The estimation and inference of human predictive uncertainty have great potential to improve the sampling efficiency and prediction reliability of human-in-the-loop systems for smart healthcare, smart education, and human-computer interactions. Predictive uncertainty in humans is highly interpretabl…
Adversarial learning strategy has demonstrated remarkable performance in dealing with single-source Domain Adaptation (DA) problems, and it has recently been applied to Multi-source DA (MDA) problems. Although most existing MDA strategies rely on a multiple domain discriminator setting, its effect o…
While multi-step adversarial training is widely popular as an effective defense method against strong adversarial attacks, its computational cost is notoriously expensive, compared to standard training. Several single-step adversarial training methods have been proposed to mitigate the above-mention…
Due to the complexity of emotional features, there has been limited success in emotional voice conversion. One major challenge is that conversion between more than two kinds of emotions often accompanies distortion of voice signal.The factorized hierarchical variational autoencoder (FHVAE) [1] was p…
Recent attention-based end-to-end speech synthesis from text systems have achieved human-level performance. However, many approaches cause a sequence-to-sequence model to generate only averaged results of the input text, making it difficult to control the duration of utterance. In this study, we pre…
This paper presents a novel approach to improve the performance of polyphonic sound event detection that combines a convolutional bidirectional recurrent neural network (CBRNN) with transfer learning. The ordinary convolutional recurrent neural network (CRNN) is known to suffer from a vanishing grad…