The evaluation of object detection models is usually performed by optimizing a single metric, e. g. mAP, on a fixed set of datasets, e. g. Microsoft COCO and Pascal VOC.
Language-guided image editing has achieved great success recently.
Our channel-independent patch time series Transformer (PatchTST) can improve the long-term forecasting accuracy significantly when compared with that of SOTA Transformer-based models.
We present SinDiffusion, leveraging denoising diffusion models to capture internal distribution of patches from a single natural image.
Ranked #1 on Image Generation on Places50
While deep learning models have replaced hand-designed features across many domains, these models are still trained with hand-designed optimizers.
We apply the resulting learned optimizer to a variety of neural network training tasks, where it outperforms the current state of the art learned optimizer -- at matched optimizer computational overhead -- with regard to optimization performance and meta-training speed, and is capable of generalization to tasks far different from those it was meta-trained on.
Generative Adversarial Networks (GANs) are widely adapted for anonymization of human figures.