6 research outputs found
TD-GEM: Text-Driven Garment Editing Mapper
Language-based fashion image editing allows users to try out variations of
desired garments through provided text prompts. Inspired by research on
manipulating latent representations in StyleCLIP and HairCLIP, we focus on
these latent spaces for editing fashion items of full-body human datasets.
Currently, there is a gap in handling fashion image editing due to the
complexity of garment shapes and textures and the diversity of human poses. In
this paper, we propose an editing optimizer scheme method called Text-Driven
Garment Editing Mapper (TD-GEM), aiming to edit fashion items in a disentangled
way. To this end, we initially obtain a latent representation of an image
through generative adversarial network inversions such as Encoder for Editing
(e4e) or Pivotal Tuning Inversion (PTI) for more accurate results. An
optimization-based Contrasive Language-Image Pre-training (CLIP) is then
utilized to guide the latent representation of a fashion image in the direction
of a target attribute expressed in terms of a text prompt. Our TD-GEM
manipulates the image accurately according to the target attribute, while other
parts of the image are kept untouched. In the experiments, we evaluate TD-GEM
on two different attributes (i.e., "color" and "sleeve length"), which
effectively generates realistic images compared to the recent manipulation
schemes.Comment: The first two authors contributed equall
Model-free neural network-based predictive control for robust operation of power converters
An accurate definition of a system model significantly affects the performance of model-based control strategies, for example, model predictive control (MPC). In this paper, a model-free predictive control strategy is presented to mitigate all ramifications of the model’s uncertainties and parameter mismatch between the plant and controller for the control of power electronic converters in applications such as microgrids. A specific recurrent neural network structure called state-space neural network (ssNN) is proposed as a model-free current predictive control for a three-phase power converter. In this approach, NN weights are updated through particle swarm optimization (PSO) for faster convergence. After the training process, the proposed ssNN-PSO combined with the predictive controller using a performance criterion overcomes parameter variations in the physical system. A comparison has been carried out between the conventional MPC and the proposed model-free predictive control in different scenarios. The simulation results of the proposed control scheme exhibit more robustness compared to the conventional finite-control-set MPC