On Self-Supervised Image Representations for GAN Evaluation
The embeddings from CNNs pretrained on Imagenet classification are de-facto standard image representations for assessing GANs via FID, Precision and Recall measures. Despite broad previous criticism of their usage for non-Imagenet domains, these embeddings are still the top choice in most of the GAN literature. In this paper, we advocate the usage of the state-of-the-art self-supervised representations to evaluate GANs on the established non-Imagenet benchmarks. These representations, typically obtained via contrastive learning, are shown to provide better transfer to new tasks and domains, therefore, can serve as more universal embeddings of natural images. With extensive comparison of the recent GANs on the common datasets, we show that self-supervised representations produce a more reasonable ranking of models in terms of FID/Precision/Recall, while the ranking with classification-pretrained embeddings often can be misleading.
Code (0)
등록된 구현이 없습니다.
Tasks
Contrastive LearningGeneral ClassificationSimilar Papers 제목 키워드 기반
CompRess: Self-Supervised Learning by Compressing Representations
Self-supervised learning aims to learn good representations with unlabeled data. Recent works have shown that larger models benefit more from self-supervised learning than smaller models. As a result, the gap between sup…
Linear evaluationModel CompressionSelf-Supervised Image ClassificationSelfAugment: Automatic Augmentation Policies for Self-Supervised Learning
A common practice in unsupervised representation learning is to use labeled data to evaluate the quality of the learned representations. This supervised evaluation is then used to guide critical aspects of the training p…
Data AugmentationRepresentation LearningSelf-Supervised LearningSSFD: Self-Supervised Feature Distance as an MR Image Reconstruction Quality Metric
Evaluation of accelerated magnetic resonance imaging (MRI) reconstruction methods is imperfect due to the discordance between quantitative image quality metrics and radiologist-perceived image quality. Self-supervised l…
Image ReconstructionMRI ReconstructionSelf-Supervised LearningSSIMGeometric Analysis of Self-Supervised Vision Representations for Semantic Image Retrieval
Content-based image retrieval (CBIR) systems enable users to search images based on visual content instead of relying on metadata. The text domain has benefited from vector search of representations created with unsuperv…
Content-Based Image RetrievalSelf-Supervised LearningSemantic RetrievalEBMs vs. CL: Exploring Self-Supervised Visual Pretraining for Visual Question Answering
The availability of clean and diverse labeled data is a major roadblock for training models on complex tasks such as visual question answering (VQA). The extensive work on large vision-and-language models has shown that …
Contrastive LearningOut of Distribution (OOD) DetectionQuestion AnsweringSelf-Supervised Learning+3