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Found 5 papers, 1 papers with code
What and Where: Learn to Plug Adapters via NAS for Multi-Domain Learning
As an important and challenging problem, multi-domain learning (MDL) typically seeks for a set of effective lightweight domain-specific adapter modules plugged into a common domain-agnostic network.
Epoch-evolving Gaussian Process Guided Learning
In this paper, we propose a novel learning scheme called epoch-evolving Gaussian Process Guided Learning (GPGL), which aims at characterizing the correlation information between the batch-level distribution and the global data distribution.
A Survey on Generative Adversarial Networks: Variants, Applications, and Training
We survey, (I) the original GAN model and its modified classical versions, (II) detail analysis of various GAN applications in different domains, (III) detail study about the various GAN training obstacles as well as training solutions.
ResKD: Residual-Guided Knowledge Distillation
In this paper, we see knowledge distillation in a fresh light, using the knowledge gap, or the residual, between a teacher and a student as guidance to train a much more lightweight student, called a res-student.
BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation
Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously.
