Add to ORKGThis work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge transfer is achieved by joint optimization of self-supervised learning pseudo task and supervised learning task, improving supervised learning task performance. Recent progress in self-supervised learning uses a large volume of data, which becomes a constraint for its applications on small-scale datasets. This work shares a simple yet effective joint training framework that reinforces human-supervised task learning by learning self-supervised representations just-in-time and vice versa. Experiments on three public datasets from different visual domains, Intel Image, ...
Recent approaches in self-supervised learning of image representations can be categorized into diffe...
Recent approaches in self-supervised learning of image representations can be categorized into diffe...
Recent approaches in self-supervised learning of image representations can be categorized into diffe...
This work investigates the unexplored usability of self-supervised representation learning in the di...
In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset witho...
Self-supervised representation learning methods aim to provide powerful deep feature learning withou...
Knowledge distillation often involves how to define and transfer knowledge from teacher to student e...
The complexity of any information processing task is highly dependent on the space where data is rep...
Self-supervised learning is a powerful paradigm for representation learning on unlabelled images. A ...
This thesis investigates the possibility of efficiently adapting self-supervised representation lear...
State-of-the-art frameworks in self-supervised learning have recently shown that fully utilizing tra...
Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which preven...
Recently, self-supervised representation learning gives further development in multimedia technology...
Self-supervised learning (SSL) aims at extracting from abundant unlabelled images transferable seman...
Prior works on self-supervised pre-training focus on the joint training scenario, where massive unla...
Recent approaches in self-supervised learning of image representations can be categorized into diffe...
Recent approaches in self-supervised learning of image representations can be categorized into diffe...
Recent approaches in self-supervised learning of image representations can be categorized into diffe...
This work investigates the unexplored usability of self-supervised representation learning in the di...
In self-supervised learning, one trains a model to solve a so-called pretext task on a dataset witho...
Self-supervised representation learning methods aim to provide powerful deep feature learning withou...
Knowledge distillation often involves how to define and transfer knowledge from teacher to student e...
The complexity of any information processing task is highly dependent on the space where data is rep...
Self-supervised learning is a powerful paradigm for representation learning on unlabelled images. A ...
This thesis investigates the possibility of efficiently adapting self-supervised representation lear...
State-of-the-art frameworks in self-supervised learning have recently shown that fully utilizing tra...
Existing few-shot learning (FSL) methods rely on training with a large labeled dataset, which preven...
Recently, self-supervised representation learning gives further development in multimedia technology...
Self-supervised learning (SSL) aims at extracting from abundant unlabelled images transferable seman...
Prior works on self-supervised pre-training focus on the joint training scenario, where massive unla...
Recent approaches in self-supervised learning of image representations can be categorized into diffe...
Recent approaches in self-supervised learning of image representations can be categorized into diffe...
Recent approaches in self-supervised learning of image representations can be categorized into diffe...