6 datasets found

Spot-the-difference self-supervised pre-training for anomaly detection and segmentation.

The proposed self-supervised speech quality estimator trained only on clean speech.

The dataset is used for training a language model to follow behavioral principles without the use of preference labels, demonstrations, or human oversight.

The dataset used in this paper is a self-supervised audio pre-training framework called HuBERT.

Self-supervised dual-channel attentive network for session-based social recommendation.

Image captioning, a fundamental task in vision-language understanding, seeks to generate accurate natural language descriptions for provided images. Current image captioning...