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Found 16 papers, 10 papers with code
An Introduction to Vision-Language Modeling
Then, we present and discuss approaches to evaluate VLMs.
A Picture is Worth More Than 77 Text Tokens: Evaluating CLIP-Style Models on Dense Captions
Curation methods for massive vision-language datasets trade off between dataset size and quality.
Feedback-guided Data Synthesis for Imbalanced Classification
In this work, we introduce a framework for augmenting static datasets with useful synthetic samples, which leverages one-shot feedback from the classifier to drive the sampling of the generative model.
PUG: Photorealistic and Semantically Controllable Synthetic Data for Representation Learning
Synthetic image datasets offer unmatched advantages for designing and evaluating deep neural networks: they make it possible to (i) render as many data samples as needed, (ii) precisely control each scene and yield granular ground truth labels (and captions), (iii) precisely control distribution shifts between training and testing to isolate variables of interest for sound experimentation.
Stochastic positional embeddings improve masked image modeling
Masked Image Modeling (MIM) is a promising self-supervised learning approach that enables learning from unlabeled images.
Do SSL Models Have Déjà Vu? A Case of Unintended Memorization in Self-supervised Learning
Self-supervised learning (SSL) algorithms can produce useful image representations by learning to associate different parts of natural images with one another.
Objectives Matter: Understanding the Impact of Self-Supervised Objectives on Vision Transformer Representations
Here, we aim to explain these differences by analyzing the impact of these objectives on the structure and transferability of the learned representations.
A Cookbook of Self-Supervised Learning
Self-supervised learning, dubbed the dark matter of intelligence, is a promising path to advance machine learning.
A surprisingly simple technique to control the pretraining bias for better transfer: Expand or Narrow your representation
Self-Supervised Learning (SSL) models rely on a pretext task to learn representations.
Towards Democratizing Joint-Embedding Self-Supervised Learning
Joint Embedding Self-Supervised Learning (JE-SSL) has seen rapid developments in recent years, due to its promise to effectively leverage large unlabeled data.
The Hidden Uniform Cluster Prior in Self-Supervised Learning
A successful paradigm in representation learning is to perform self-supervised pretraining using tasks based on mini-batch statistics (e. g., SimCLR, VICReg, SwAV, MSN).
Guillotine Regularization: Why removing layers is needed to improve generalization in Self-Supervised Learning
This is a little vexing, as one would hope that the network layer at which invariance is explicitly enforced by the SSL criterion during training (the last projector layer) should be the one to use for best generalization performance downstream.
Masked Siamese Networks for Label-Efficient Learning
We propose Masked Siamese Networks (MSN), a self-supervised learning framework for learning image representations.
Self-Supervised Image Classification
Self-Supervised Learning
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High Fidelity Visualization of What Your Self-Supervised Representation Knows About
Discovering what is learned by neural networks remains a challenge.
Evaluation of generative networks through their data augmentation capacity
This mixture of real and generated data is thus used to train a classifier which is afterwards tested on a given labeled test dataset.
Learning to Generate Samples from Noise through Infusion Training
In this work, we investigate a novel training procedure to learn a generative model as the transition operator of a Markov chain, such that, when applied repeatedly on an unstructured random noise sample, it will denoise it into a sample that matches the target distribution from the training set.
