Self-Supervised Image Classification
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Benchmarks
Most implemented
A Simple Framework for Contrastive Learning of Visual Representations
Masked Autoencoders Are Scalable Vision Learners
Momentum Contrast for Unsupervised Visual Representation Learning
Colorful Image Colorization
Improved Baselines with Momentum Contrastive Learning
Emerging Properties in Self-Supervised Vision Transformers
Papers
Stabilize the Latent Space for Image Autoregressive Modeling: A Unified Perspective
Latent-based image generative models, such as Latent Diffusion Models (LDMs) and Mask Image Models (MIMs), have achieved notable success in image generation tasks. These models typically leverage reconstructive autoencod…
Conditional Image GenerationImage GenerationLinear-Probe ClassificationSelf-Supervised Image Classification+2SynCo: Synthetic Hard Negatives in Contrastive Learning for Better Unsupervised Visual Representations
Contrastive learning has become a dominant approach in self-supervised visual representation learning. Hard negatives - samples closely resembling the anchor - are key to enhancing learned representations' discriminative…
Contrastive LearningImage ClassificationImage SegmentationInstance Segmentation+8Unsupervised Representation Learning by Balanced Self Attention Matching
Many leading self-supervised methods for unsupervised representation learning, in particular those for embedding image features, are built on variants of the instance discrimination task, whose optimization is known to b…
Representation LearningSelf-Supervised Image ClassificationSelf-Supervised Image Classification on ImageNetTransfer LearningMulti-label Cluster Discrimination for Visual Representation Learning
Contrastive Language Image Pre-training (CLIP) has recently demonstrated success across various tasks due to superior feature representation empowered by image-text contrastive learning. However, the instance discriminat…
Contrastive LearningImage-text RetrievalMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATION+8Estimating Physical Information Consistency of Channel Data Augmentation for Remote Sensing Images
The application of data augmentation for deep learning (DL) methods plays an important role in achieving state-of-the-art results in supervised, semi-supervised, and self-supervised image classification. In particular, c…
Data Augmentationimage-classificationImage ClassificationMulti-Label Image Classification+1IPCL: Iterative Pseudo-Supervised Contrastive Learning to Improve Self-Supervised Feature Representation
Self-supervised learning with a contrastive batch approach has become a powerful tool for representation learning in computer vision. The performance of downstream tasks is proportional to the quality of visual features …
Contrastive LearningData Augmentationimage-classificationImage Classification+5