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Found 6 papers, 4 papers with code
DiLM: Distilling Dataset into Language Model for Text-level Dataset Distillation
To address this issue, we propose a novel text dataset distillation approach, called Distilling dataset into Language Model (DiLM), which trains a language model to generate informative synthetic training samples as text data, instead of directly optimizing synthetic samples.
Personalized Image Enhancement Featuring Masked Style Modeling
Second, to allow this model to consider the contents of images, we propose a novel training scheme where we download images from Flickr and create pseudo input and retouched image pairs using a degrading model.
Crowd-Powered Photo Enhancement Featuring an Active Learning Based Local Filter
Existing photo enhancement methods are either not content-aware or not local; therefore, we propose a crowd-powered local enhancement method for content-aware local enhancement, which is achieved by asking crowd workers to locally optimize parameters for image editing functions.
Self-Play Reinforcement Learning for Fast Image Retargeting
In this study, we address image retargeting, which is a task that adjusts input images to arbitrary sizes.
Unpaired Image Enhancement Featuring Reinforcement-Learning-Controlled Image Editing Software
This paper tackles unpaired image enhancement, a task of learning a mapping function which transforms input images into enhanced images in the absence of input-output image pairs.
Object-Aware Instance Labeling for Weakly Supervised Object Detection
Weakly supervised object detection (WSOD), where a detector is trained with only image-level annotations, is attracting more and more attention.
