We introduce SynCLR a novel approach for learning visual representations exclusively from
synthetic images without any real data. We synthesize a large dataset of image captions …

The power of DNNs relies heavily on the quantity and quality of training data. However,
collecting and annotating data on a large scale is often expensive and time-consuming. To …

Abstract Vision Language Model (VLM) is a popular research field located at the fusion of
computer vision and natural language processing (NLP). With the emergence of transformer …

Spiking Neural Networks (SNNs) have attracted significant attention from researchers across
various domains due to their brain-inspired information processing mechanism. However …

Generating transferable adversarial examples is a challenging issue in adversarial attacks.
Existing works on transferable adversarial examples generation mainly focus on models …

Deep neural networks are known to be vulnerable to security risks due to the inherent
transferable nature of adversarial examples. Despite the success of recent generative model …

Self-supervised learning aims to learn representation that can be effectively generalized to
downstream tasks. Many self-supervised approaches regard two views of an image as both …

Data augmentation improves the generalization power of deep learning models by
synthesizing more training samples. Sample-mixing is a popular data augmentation …

The objective of content-based image retrieval (CBIR) is to locate samples from a database
that are akin to a query, relying on the content embedded within the images. A contemporary …

We explore the impact of training with more diverse datasets, characterized by the number of
unique samples, on the performance of self-supervised learning (SSL) under a fixed …