Microsoft launches 3 new AI models in direct shot at OpenAI and Google

Microsoft launches three in-house AI models for transcription, voice, and image generation, challenging OpenAI and Google ...
IEEE

DTSF-CDNet: A Dual-Branch Encoder-Decoder Network with Differential Transformer Skip Fusion for Image Change Detection

Abstract: Change detection plays a vital role in numerous real-world domains, aiming to accurately identify regions that have changed between two temporally distinct images. Capturing the complex ...
Frontiers

A combined approach to lithology identification using reinforcement learning and transformer algorithms

Lithology identification plays a pivotal role in logging interpretation during drilling operations, directly influencing drilling decisions and efficiency. Conventional lithology identification ...
Scientific Research Publishing

From U-Net to Swin-Unet Transformers: The Next-Generation Advances in Brain Tumor Segmentation with Deep Learning ()

Brain tumor segmentation is a vital step in diagnosis, treatment planning, and prognosis in neuro-oncology. In recent years, deep learning approaches have revolutionized this field, evolving from the ...
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 ...
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 ...