Related papers: Using Style Ambiguity Loss to Improve Aesthetics of Diffusion Models

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Using Multimodal Foundation Models and Clustering for Improved Style Ambiguity Loss [0.0]
We explore a new form of the style ambiguity training objective, used to approximate creativity, that does not require training a classifier or even a labeled dataset. We find our new methods improve upon the traditional method, based on automated metrics for human judgment, while still maintaining creativity and novelty.
arXiv Detail & Related papers (2024-06-20T15:43:13Z)
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We propose MuseumMaker, a method that enables the synthesis of images by following a set of customized styles in a never-end manner. When facing with a new customization style, we develop a style distillation loss module to extract and learn the styles of the training data for new image generation. It can minimize the learning biases caused by content of new training images, and address the catastrophic overfitting issue induced by few-shot images.
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We present a framework for understanding and extracting style descriptors from images. Our framework comprises a new dataset curated using the insight that style is a subjective property of an image. We also propose a method to extract style attribute descriptors that can be used to style of a generated image to the images used in the training dataset of a text-to-image model.
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arXiv Detail & Related papers (2023-05-22T17:57:41Z)
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Large-scale text-to-image diffusion models can generate high-fidelity images with powerful compositional ability. These models are typically trained on an enormous amount of Internet data, often containing copyrighted material, licensed images, and personal photos. We propose an efficient method of ablating concepts in the pretrained model, preventing the generation of a target concept.
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We present a novel method of learning style-agnostic representation using both style transfer and adversarial learning. Our method trains the actor with diverse image styles generated from an inherent adversarial style generator. We verify that our method achieves competitive or better performances than the state-of-the-art approaches on Procgen and Distracting Control Suite benchmarks.
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We propose a novel adversarial style augmentation approach, which can generate hard stylized images during training. Experiments on two synthetic-to-real semantic segmentation benchmarks demonstrate that AdvStyle can significantly improve the model performance on unseen real domains.
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arXiv Detail & Related papers (2022-01-04T06:55:19Z)
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Several incremental learning methods are proposed to mitigate catastrophic forgetting for object detection. Despite the effectiveness, these methods require co-occurrence of the unlabeled base classes in the training data of the novel classes. We propose the use of unlabeled in-the-wild data to bridge the non-occurrence caused by the missing base classes during the training of additional novel classes.
arXiv Detail & Related papers (2021-10-28T10:57:25Z)
Towards Good Practices for Efficiently Annotating Large-Scale Image Classification Datasets [90.61266099147053]
We investigate efficient annotation strategies for collecting multi-class classification labels for a large collection of images. We propose modifications and best practices aimed at minimizing human labeling effort. Simulated experiments on a 125k image subset of the ImageNet100 show that it can be annotated to 80% top-1 accuracy with 0.35 annotations per image on average.
arXiv Detail & Related papers (2021-04-26T16:29:32Z)
A Closer Look at Self-training for Zero-Label Semantic Segmentation [53.4488444382874]
Being able to segment unseen classes not observed during training is an important technical challenge in deep learning. Prior zero-label semantic segmentation works approach this task by learning visual-semantic embeddings or generative models. We propose a consistency regularizer to filter out noisy pseudo-labels by taking the intersections of the pseudo-labels generated from different augmentations of the same image.
arXiv Detail & Related papers (2021-04-21T14:34:33Z)
Hidden Footprints: Learning Contextual Walkability from 3D Human Trails [70.01257397390361]
Current datasets only tell you where people are, not where they could be. We first augment the set of valid, labeled walkable regions by propagating person observations between images, utilizing 3D information to create what we call hidden footprints. We devise a training strategy designed for such sparse labels, combining a class-balanced classification loss with a contextual adversarial loss.
arXiv Detail & Related papers (2020-08-19T23:19:08Z)
Automatically Discovering and Learning New Visual Categories with Ranking Statistics [145.89790963544314]
We tackle the problem of discovering novel classes in an image collection given labelled examples of other classes. We learn a general-purpose clustering model and use the latter to identify the new classes in the unlabelled data. We evaluate our approach on standard classification benchmarks and outperform current methods for novel category discovery by a significant margin.
arXiv Detail & Related papers (2020-02-13T18:53:32Z)

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