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Found 9 papers, 5 papers with code
Neural Meta-Symbolic Reasoning and Learning
To make deep learning do more from less, we propose the first neural meta-symbolic system (NEMESYS) for reasoning and learning: meta programming using differentiable forward-chaining reasoning in first-order logic.
SSD: Towards Better Text-Image Consistency Metric in Text-to-Image Generation
Generating consistent and high-quality images from given texts is essential for visual-language understanding.
Rebalanced Zero-shot Learning
Broadly speaking, present ZSL methods usually adopt class-level semantic labels and compare them with instance-level semantic predictions to infer unseen classes.
A Unified Perspective on Natural Gradient Variational Inference with Gaussian Mixture Models
The two currently most effective methods for GMM-based variational inference, VIPS and iBayes-GMM, both employ independent natural gradient updates for the individual components and their weights.
A First-Order Method for Estimating Natural Gradients for Variational Inference with Gaussians and Gaussian Mixture Models
Effective approaches for Gaussian variational inference are MORE, VOGN, and VON, which are zero-order, first-order, and second-order, respectively.
Disentangling Semantic-to-visual Confusion for Zero-shot Learning
However, the traditional TL cannot search reliable unseen disentangled representations due to the unavailability of unseen classes in ZSL.
Multi-Domain Multi-Task Rehearsal for Lifelong Learning
Rehearsal, seeking to remind the model by storing old knowledge in lifelong learning, is one of the most effective ways to mitigate catastrophic forgetting, i. e., biased forgetting of previous knowledge when moving to new tasks.
Associating Multi-Scale Receptive Fields for Fine-grained Recognition
First, CNL computes correlations between features of a query layer and all response layers.
SR-GAN: Semantic Rectifying Generative Adversarial Network for Zero-shot Learning
First, we pre-train a Semantic Rectifying Network (SRN) to rectify semantic space with a semantic loss and a rectifying loss.
