feature space
A multi-dimensional representation of data where each dimension corresponds to a distinct feature, allowing models to learn relationships and patterns within the data.
- Approximate Domain Unlearning for Vision-Language Models
- Controlled Visual Hallucination via Thalamus-Driven Decoupling Network for Domain Adaptation of Black-Box Predictors
- Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clustering
- Long-Tailed Recognition via Information-Preservable Two-Stage Learning
- Non-Adaptive Adversarial Face Generation
- Squared families are useful conjugate priors
- Understanding Fairness and Prediction Error through Subspace Decomposition and Influence Analysis