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Transformers’ Encoder Architecture Explained — No Phd Needed!

We break down the Encoder architecture in Transformers, layer by layer! If you've ever wondered how models like BERT and GPT process text, this is your ultimate guide. We look at the entire design of ...
IEEE

Medical Report Generation With Knowledge Distillation and Multi-Stage Hierarchical Attention in Vision Transformer Encoder and GPT-2 Decoder

Abstract: Automated medical report generation is a challenging task that involves synthesizing diagnostic findings and clinical observations from medical images. In this study, we propose a novel ...
Scientific Research Publishing

Chen, J., Lu, Y., Yu, Q., et al. (2021) Transunet: Transformers Make Strong Encoders for Medical Image Segmentation.

ABSTRACT: To address the challenges of morphological irregularity and boundary ambiguity in colorectal polyp image segmentation, we propose a Dual-Decoder Pyramid Vision Transformer Network (DDPVT-Net ...
GitHub

Understanding Self-Attention(Encoder's Self-Attention and Decoder's Masked Self-Attention) in Transformers

- Driven by the **output**, attending to the **input**. - Each word in the output sequence determines which parts of the input sequence to attend to, forming an **output-oriented attention** mechanism ...
marktechpost

Decoupled Diffusion Transformers: Accelerating High-Fidelity Image Generation via Semantic-Detail Separation and Encoder Sharing

Diffusion Transformers have demonstrated outstanding performance in image generation tasks, surpassing traditional models, including GANs and autoregressive architectures. They operate by gradually ...
marktechpost

Google DeepMind Research Releases SigLIP2: A Family of New Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features

Modern vision-language models have transformed how we process visual data, yet they often fall short when it comes to fine-grained localization and dense feature extraction. Many traditional models ...
GitHub

TSDAE layer initialization of encoder and decoder

I want to train pretrain a sentence transformer using TSDAE. We have previously used all-MiniLM-L6-v2 as a checkpoint where we finetuned with MultipleNegativeRankingLoss with the main downstream task ...