WebApr 12, 2024 · HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization Sungyeon Kim · Boseung Jeong · Suha Kwak Bi-directional Distribution Alignment for … Web1 day ago · Data scarcity is a major challenge when training deep learning (DL) models. DL demands a large amount of data to achieve exceptional performance. Unfortunately, many applications have small or inadequate data to train DL frameworks. Usually, manual labeling is needed to provide labeled data, which typically involves human annotators with a vast …
Sungyeon Kim DeepAI
WebWe present a novel self-taught framework for unsuper-vised metric learning, which alternates between predicting class-equivalence relations between data through a mov … WebSelf-supervised learning works in the absence of labels and thus eliminates the negative impact of noisy labels. Motivated by co-training with both supervised learning view and … rs3 totem of crystal top
GitHub - tjddus9597/STML-CVPR22: Official PyTorch Implementation o…
WebMar 27, 2024 · Experiments on metric learning benchmarks demonstrate that our method largely improves performance, or reduces sizes and output dimensions of target models effectively. We further show that it can be also used to enhance quality of self-supervised representation and performance of classification models. ... Self-Taught Metric Learning … WebApr 12, 2024 · HIER: Metric Learning Beyond Class Labels via Hierarchical Regularization Sungyeon Kim · Boseung Jeong · Suha Kwak Bi-directional Distribution Alignment for Transductive Zero Shot Learning Zhicai Wang · YANBIN HAO · Tingting Mu · Ouxiang Li · Shuo Wang · Xiangnan He WebSelf-Taught Metric Learning. Contextualized semantic similarity between a pair of data is estimated on the embedding space of the teacher network. The semantic similarity is then used as a pseudo label, and the student network is optimized by relaxed contrastive loss with KL divergence. The teacher network is updated by an exponential moving ... rs3 toy battleship