它也非常接近“上限”,该“上限”以像素生成期间 ImageNet 真实图像的 Oracle 表示为条件,证明了 RDM 在生成真实 SSL 表示方面的有效性。 自调节图像生成方面的先前工作主要集中于将图像分类为表示空间内的簇,使用这些簇作为伪类调节[3,34,40]。我们还评估了这种基于聚类的调节在 RCG 中的性能,在 Moco v3 ViT-...
RCG: Self-conditioned Image Generation via Generating Representations TL; DR:将图像的无监督表征作为(自)条件(而非是将文本 prompt 作为条件),生成与原图语义内容一致的多样且高质量结果。视觉训练能不能 / 需不需要摆脱文本,仍有待研究。 引言 就像图像自监督表征学习(对比学习 / 图像掩码建模)成功赶超了有监...
self-supervised learning (SSL) 缩小了和监督学习的性能差,受此启发,本文提出 self-conditioned image generation,不同于传统的无条件生成,不是直接从噪声到图片,而是多了个中间的表征。 作者认为自条件图片生成有如下重要作用: 比无条件图片生成更符合直觉,反映了艺术家在作画之前的构思过程 类似超越了监督学习的无...
Secondly, to alleviate the gap between the layout elements domain and the visual domain, we design a Geometry Alignment module, in which the geometric information of the image is aligned with the layout representation. Thirdly, we present a self-refinement mechanism to automatically refine the ...
The conditional diffusion models have made significant progress in image synthesis, leveraging human annotations such as class labels or text descriptions to guide the generative process. However, different from image synthesis, low-light image enhancement(LLIE) lacks strictly calibrated conditional priors ...
conda create -c conda-forge -n flowsite rdkit python pip install torch torchvision torchaudio pip install torch_geometric pip install pyyaml wandb biopython spyrmsd einops biopandas plotly prody tqdm lightning imageio pip install e3nn pip install torch_scatter torch_sparse torch_cluster -f https...
image processing,natural languages,navigation,neural nets,road traffic,traffic engineering computingWe propose a novel pipeline that blends encodings from natural language and 3D semantic maps obtained from visual imagery to generate local trajectories that are executed by a low-level controller. The ...
Self-conditioned Image Generation via Generating Representations 单位:MIT, Meta(何恺明) 代码:github.com/LTH14/rcg 论文:arxiv.org/abs/2312.0370 本文提出了表示条件图像生成(RCG):一种简单而有效的图像生成框架,为类无条件图像生成树立了新的基准。 RCG 不以任何人工注释为条件。相反,它以自监督表示分布为条件...
Diverse Image Generation via Self-Conditioned GANs学习笔记stevliu/self-conditioned-ganpaper 解决问题 生成对抗网络(GAN)在无人监督的情况下,基于标签数据训练的conditional GAN与未经任何标签训练的uncondi…
(self, noise, labels): # Concatenate label embedding and image to produce input gen_input = torch.cat((self.label_emb(labels), noise), -1) img = self.model(gen_input) img = img.view(img.size(0), *img_shape) return img class Discriminator(nn.Module): def __init__(self): super...