Search Results for author:
Found 7 papers, 2 papers with code
Friendly Topic Assistant for Transformer Based Abstractive Summarization
Abstractive document summarization is a comprehensive task including document understanding and summary generation, in which area Transformer-based models have achieved the state-of-the-art performance.
Hierarchical Vector Quantized Transformer for Multi-class Unsupervised Anomaly Detection
First, instead of learning the continuous representations, we preserve the typical normal patterns as discrete iconic prototypes, and confirm the importance of Vector Quantization in preventing the model from falling into the shortcut.
Prototypes-oriented Transductive Few-shot Learning with Conditional Transport
Transductive Few-Shot Learning (TFSL) has recently attracted increasing attention since it typically outperforms its inductive peer by leveraging statistics of query samples.
Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport
A recent solution to this problem is calibrating the distribution of these few sample classes by transferring statistics from the base classes with sufficient examples, where how to decide the transfer weights from base classes to novel classes is the key.
Learning Prototype-oriented Set Representations for Meta-Learning
Since our plug-and-play framework can be applied to many meta-learning problems, we further instantiate it to the cases of few-shot classification and implicit meta generative modeling.
Variational Hetero-Encoder Randomized GANs for Joint Image-Text Modeling
For bidirectional joint image-text modeling, we develop variational hetero-encoder (VHE) randomized generative adversarial network (GAN), a versatile deep generative model that integrates a probabilistic text decoder, probabilistic image encoder, and GAN into a coherent end-to-end multi-modality learning framework.
VHEGAN: Variational Hetero-Encoder Randomized GAN for Zero-Shot Learning
To extract and relate visual and linguistic concepts from images and textual descriptions for text-based zero-shot learning (ZSL), we develop variational hetero-encoder (VHE) that decodes text via a deep probabilisitic topic model, the variational posterior of whose local latent variables is encoded from an image via a Weibull distribution based inference network.
