Asia
Domain Re-Modulation for Few-Shot Generative Domain Adaptation Yi Wu, Ziqiang Li University of Science and Technology of China Chaoyue Wang, Heliang Zheng, Shanshan Zhao JD Explore Academy Bin Li
In this study, we delve into the task of few-shot Generative Domain Adaptation (GDA), which involves transferring a pre-trained generator from one domain to a new domain using only a few reference images. Inspired by the way human brains acquire knowledge in new domains, we present an innovative generator structure called Domain Re-Modulation (DoRM) .
Supplementary Materials: Semi-Supervised Contrastive Learning for Deep Regression with Ordinal Rankings from Spectral Seriation
The main result is presented in Theorem 2. According to the definition of the Fiedler vector, we have ( L + L)( f + f) = ( ฮป + ฮป)( f + f). We outline the proof below for interested readers. The main result is presented in Theorem 2. We first present Stewart's theorem in Lemma 1 to assist Actual times may differ depending on hardware and environment. We also show the number of model parameters required for each method in Table S3. Hyper-parameters were selected based on a coarse search on the validation set.
The emergence of clusters in self-attention dynamics
We show that particles, representing tokens, tend to cluster toward particular limiting objects as time tends to infinity.
RegExplainer: Generating Explanations for Graph Neural Networks in Regression Tasks
Graph regression is a fundamental task that has gained significant attention in various graph learning tasks.
