Tag: self_supervised

• Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency (04 Oct 2023)
  

  This is my reading note for Leveraging Unpaired Data for Vision-Language Generative Models via Cycle Consistency. The papers proposes a method to train a multi modality model between text and image. Especially, the paper propose cycle consistency loss to leverage unpaired text and image: use image to generate text and use text to recover image and vice verse. It reminds me cycle-GAN paper.

• SeamlessM4T-Massively Multilingual & Multimodal Machine Translation (05 Sep 2023)
  

  This is my reading note 2/2 on SeamlessM4T-Massively Multilingual & Multimodal Machine Translation. It is end to end multi language translation system supports multimodality (text and audio). This paper also provides a good review on machine translation. This note focus on data preparation part of the paper and please read SeamlessM4T-data for the other part.

• SeamlessM4T-Massively Multilingual & Multimodal Machine Translation (04 Sep 2023)
  

  This is my reading note 1/2 on SeamlessM4T-Massively Multilingual & Multimodal Machine Translation. It is end to end multi language translation system supports multimodality (text and audio). This paper also provides a good review on machine translation. This note focus on data preparation part of the paper and please read SeamlessM4T-model for the other part.

• AutoCLIP Auto-tuning Zero-Shot Classifiers for Vision-Language Models (29 Jul 2023)
  

  This is my reading note for AutoCLIP: Auto-tuning Zero-Shot Classifiers for Vision-Language Models. This paper proposes a method to use clip for zero shot image classification, to do that, it first generates several prompt to convert class label to text embedding by average. Then the image is processed by visual encoder. The label of image is the one has slowest distance between label embody and image embedding. This paper propose to use soft Max instead of average for label embedding.

• 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.

• ELECTRA Pre-training Text Encoders as Discriminators Rather Than Generators (09 Jul 2023)
  

  This is my reading note ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators. This paper proposes to replace masked language modeling with the discriminator task of whether the token is from the authentic data distribution or fixed by the generator model. Especially the model contains a generator that’s trained with masked language modeling objects and discriminator to classify whether a token is filled by the generator or not.