Search Results for author:
Found 7 papers, 1 papers with code
Partial-label Learning with Mixed Closed-set and Open-set Out-of-candidate Examples
In this paper, we term the examples whose true label is outside the candidate label set OOC (out-of-candidate) examples, and pioneer a new PLL study to learn with OOC examples.
Candidate-aware Selective Disambiguation Based On Normalized Entropy for Instance-dependent Partial-label Learning
In this paper, we start with an empirical study of the dynamics of label disambiguation in both II-PLL and ID-PLL.
Robust Loss Functions for Complementary Labels Learning
Similarly, a complementary label is also provided for each training sample in complementary-label learning.
Deep Pairwise Hashing for Cold-start Recommendation
Specifically, we first pre-train robust item representation from item content data by a Denoising Auto-encoder instead of other deterministic deep learning frameworks; then we finetune the entire framework by adding a pairwise loss objective with discrete constraints; moreover, DPH aims to minimize a pairwise ranking loss that is consistent with the ultimate goal of recommendation.
Learning unbiased zero-shot semantic segmentation networks via transductive transfer
Our method assumes that both the source images with full pixel-level labels and unlabeled target images are available during training.
Learning Cross-domain Semantic-Visual Relationships for Transductive Zero-Shot Learning
Zero-Shot Learning (ZSL) learns models for recognizing new classes.
Collaborative Generated Hashing for Market Analysis and Fast Cold-start Recommendation
Cold-start and efficiency issues of the Top-k recommendation are critical to large-scale recommender systems.
