Tag: mixup

• Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training (19 Oct 2023)
  

  This is my reading note for Filtering, Distillation, and Hard Negatives for Vision-Language Pre-Training. This paper proposes several methods to improve image-text model pre-training: 1) filtering the dataset according complexity, action and text spotting (CAT); 2) concept distillation (object category and attributes); 3) hard negative mining for contrast pairs.

• The Victim and The Beneficiary Exploiting a Poisoned Model to Train a Clean Model on Poisoned Data (19 Jul 2023)
  

  This is my reading note for The Victim and The Beneficiary: Exploiting a Poisoned Model to Train a Clean Model on Poisoned Data. This paper proposes a method to train a model which is oust to poison data attack.it contains three components: 1) use entropy to filter out poison data; 2) train a network on clean data and improve is robustness by using attention mix; 3) combine both prison data and clean data using semi-supervised learning.

• Two new Semi-supervised learning methods from Google MixMatch and UDA (07 Jun 2019)
  

  Semi-supervised learning (SSL) seeks to largely alleviate the need for labeled data by allowing a model to leverage unlabeled data. Many recent approaches for semi-supervised learning add a loss term which is computed on unlabeled data and encourages the model to generalize better to unseen data.