Papers Self-Supervised Image Classification
“Self-Supervised Image Classification” 태그가 달린 논문 110편 · 필터 해제
Self-Supervised Learning with Swin Transformers
We are witnessing a modeling shift from CNN to Transformers in computer vision. In this work, we present a self-supervised learning approach called MoBY, with Vision Transformers as its backbone architecture. The approac…
Linear evaluationobject-detectionObject DetectionSelf-Supervised Image Classification+2ResMLP: Feedforward networks for image classification with data-efficient training
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification. It is a simple residual network that alternates (i) a linear layer in which image patches interact, independently a…
Data AugmentationFine-Grained Image ClassificationGeneral Classificationimage-classification+4With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations
Self-supervised learning algorithms based on instance discrimination train encoders to be invariant to pre-defined transformations of the same instance. While most methods treat different views of the same image as posit…
Contrastive LearningFine-Grained Image ClassificationImage ClassificationSelf-Supervised Image Classification+3Emerging Properties in Self-Supervised Vision Transformers
In this paper, we question if self-supervised learning provides new properties to Vision Transformer (ViT) that stand out compared to convolutional networks (convnets). Beyond the fact that adapting self-supervised metho…
Copy DetectionImage ClassificationImage RetrievalLinear evaluation+7Mutual Contrastive Learning for Visual Representation Learning
We present a collaborative learning method called Mutual Contrastive Learning (MCL) for general visual representation learning. The core idea of MCL is to perform mutual interaction and transfer of contrastive distributi…
Contrastive LearningFew-Shot Learningimage-classificationImage Classification+6Solving Inefficiency of Self-supervised Representation Learning
Self-supervised learning (especially contrastive learning) has attracted great interest due to its huge potential in learning discriminative representations in an unsupervised manner. Despite the acknowledged successes, …
ClusteringContrastive LearningPerson Re-IdentificationRepresentation Learning+4Contrastive Learning with Stronger Augmentations
Representation learning has significantly been developed with the advance of contrastive learning methods. Most of those methods have benefited from various data augmentations that are carefully designated to maintain th…
Contrastive LearningRepresentation LearningRetrievalSelf-Supervised Image Classification+1An Empirical Study of Training Self-Supervised Vision Transformers
This paper does not describe a novel method. Instead, it studies a straightforward, incremental, yet must-know baseline given the recent progress in computer vision: self-supervised learning for Vision Transformers (ViT)…
Out-of-Distribution GeneralizationSelf-Supervised Image ClassificationSelf-Supervised LearningSelf-Supervised Classification Network
We present Self-Classifier -- a novel self-supervised end-to-end classification learning approach. Self-Classifier learns labels and representations simultaneously in a single-stage end-to-end manner by optimizing for sa…
ClassificationClusteringContrastive LearningGeneral Classification+3Barlow Twins: Self-Supervised Learning via Redundancy Reduction
Self-supervised learning (SSL) is rapidly closing the gap with supervised methods on large computer vision benchmarks. A successful approach to SSL is to learn embeddings which are invariant to distortions of the input s…
General ClassificationImage ClassificationObject DetectionSelf-Supervised Image Classification+2Self-supervised Pretraining of Visual Features in the Wild
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods. These results have been achieved in a control environment, that is the highly curated ImageNet dat…
Image ClassificationSelf-Supervised Image ClassificationSelf-Supervised LearningSemi-Supervised Image ClassificationSelf-supervised Pre-training with Hard Examples Improves Visual Representations
Self-supervised pre-training (SSP) employs random image transformations to generate training data for visual representation learning. In this paper, we first present a modeling framework that unifies existing SSP methods…
Data AugmentationRepresentation LearningSelf-Supervised Image ClassificationOBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning
Learning image representations without human supervision is an important and active research field. Several recent approaches have successfully leveraged the idea of making such a representation invariant under different…
object-detectionObject DetectionRepresentation LearningSelf-Supervised Image Classification+3A Pseudo-labelling Auto-Encoder for unsupervised image classification
In this paper, we introduce a unique variant of the denoising Auto-Encoder and combine it with the perceptual loss to classify images in an unsupervised manner. The proposed method, called Pseudo Labelling, consists of f…
ClassificationData AugmentationDenoisingimage-classification+4Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
Self-supervised learning based on instance discrimination has shown remarkable progress. In particular, contrastive learning, which regards each image as well as its augmentations as an individual class and tries to dist…
Contrastive LearningLinear evaluationRepresentation LearningSelf-Supervised Image Classification+1Boosting Contrastive Self-Supervised Learning with False Negative Cancellation
Self-supervised representation learning has made significant leaps fueled by progress in contrastive learning, which seeks to learn transformations that embed positive input pairs nearby, while pushing negative pairs far…
Contrastive LearningRepresentation LearningSelf-Supervised Image ClassificationSelf-Supervised Learning+1Exploring Simple Siamese Representation Learning
Siamese networks have become a common structure in various recent models for unsupervised visual representation learning. These models maximize the similarity between two augmentations of one image, subject to certain co…
Representation LearningSelf-Supervised Image ClassificationA comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data
In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis. However, these networks are usually trained in a supervised manner, requiring large amounts of …
image-classificationImage ClassificationImage SegmentationSegmentation+2CompRess: 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 ClassificationRepresentation Learning via Invariant Causal Mechanisms
Self-supervised learning has emerged as a strategy to reduce the reliance on costly supervised signal by pretraining representations only using unlabeled data. These methods combine heuristic proxy classification tasks w…
Contrastive LearningOut-of-Distribution GeneralizationRepresentation LearningSelf-Supervised Image Classification+1