📖 DaGAN++: Depth-Aware Generative Adversarial Network for Talking Head Video Generation

A novel self-supervised method for learning dense 3D facial geometry (ie, depth) from face videos, without requiring camera parameters and 3D geometry annotations in training.

🖥 Github: https://github.com/harlanhong/cvpr2022-dagan

⏩ Paper: https://arxiv.org/pdf/2305.06225v1.pdf

⭐️ Demo: https://huggingface.co/spaces/HarlanHong/DaGAN

📌 Dataset: https://paperswithcode.com/dataset/voxceleb1

https://t.iss.one/DataScienceT

A summary of any PDF file is now available thanks to the Transforms library, In addition, you can now ask a question to answer in the same file

🌟 By @CodeProgrammer

30% discount on subscription to the paid bot - only for the first ten subscribers

The new price is now $7 instead of $10

The bot includes more than 30 million data science books and other scientific fields, in addition to more than 80 million scientific articles

The bot has the advantage of sending books as PDF files and reading files.

The bot is easy to use, just enter the name of the book and you will get it directly as a PDF file

To subscribe to the bot: @Hussein_Sheikho

⭐️ Towards Building the Federated GPT: Federated Instruction Tuning

Shepherd: A lightweight, foundational framework enabling federated instruction tuning for large language models

🖥 Github: https://github.com/jayzhang42/federatedgpt-shepherd

⏩ Paper: https://arxiv.org/pdf/2305.05644.pdf

📌 Data Preparation: https://github.com/jayzhang42/federatedgpt-shepherd#Data_Preparation

https://t.iss.one/DataScienceT

Discover and Cure: Concept-aware Mitigation of Spurious Correlation

🖥 Github: https://github.com/wuyxin/disc

⏩ Paper: https://arxiv.org/pdf/2305.00650v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/metashift

https://t.iss.one/DataScienceT

Pre-train and Search: Efficient Embedding Table Sharding with Pre-trained Neural Cost Models

🖥 Github: https://github.com/daochenzha/neuroshard

⏩ Paper: https://arxiv.org/pdf/2305.01868v1.pdf

https://t.iss.one/DataScienceT

Multimodal Data Augmentation for Image Captioning using Diffusion Models

🖥 Github: https://github.com/xiaochr/multimodal-augmentation-image-captioning

⏩ Paper: https://arxiv.org/pdf/2305.01855v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/coco

https://t.iss.one/DataScienceT

⭐️ Towards Building the Federated GPT: Federated Instruction Tuning

Shepherd: A lightweight, foundational framework enabling federated instruction tuning for large language models

🖥 Github: https://github.com/jayzhang42/federatedgpt-shepherd

⏩ Paper: https://arxiv.org/pdf/2305.05644.pdf

📌 Data Preparation: https://github.com/jayzhang42/federatedgpt-shepherd#Data_Preparation

https://t.iss.one/DataScienceT

ULIP: Learning a Unified Representation of Language, Images, and Point Clouds for 3D Understanding

You can easily plug in any 3D backbone models and pre-train it using our framework to get a jump-start for various downstreaming tasks!

🖥 Github: https://github.com/salesforce/ulip

⏩ Paper: https://arxiv.org/abs/2305.08275v1

📌 Dataset: https://paperswithcode.com/dataset/objaverse

https://t.iss.one/DataScienceT

FastComposer: Tuning-Free Multi-Subject Image Generation with Localized Attention

FastComposer uses subject embeddings extracted by an image encoder to augment the generic text conditioning in diffusion models, enabling personalized image generation based on subject images and textual instructions with only forward passes.

🖥 Github: https://github.com/mit-han-lab/fastcomposer

⏩ Paper: https://arxiv.org/abs/2305.10431v1

📌 Dataset: https://paperswithcode.com/dataset/ffhq

⭐️ Project: https://fastcomposer.mit.edu/

https://t.iss.one/DataScienceT

Hybrid and Collaborative Passage Reranking

🖥 Github: https://github.com/zmzhang2000/hybrank

⏩ Paper: https://arxiv.org/pdf/2305.09313v1.pdf

💨 Dataset: https://paperswithcode.com/dataset/natural-questions

https://t.iss.one/DataScienceT

FunASR: A Fundamental End-to-End Speech Recognition Toolkit

FunASR, an open-source speech recognition toolkit designed to bridge the gap between academic research and industrial applications

🖥 Github: https://github.com/alibaba-damo-academy/FunASR

⭐️ Docs: https://alibaba-damo-academy.github.io/FunASR/en/index.html

⏩ Paper: https://arxiv.org/abs/2305.11013v1

📌 Dataset: https://paperswithcode.com/dataset/wenetspeech

https://t.iss.one/DataScienceT

Segment Any Anomaly without Training via Hybrid Prompt Regularization

This project addresses zero-shot anomaly detection by combining SAM and Grouding DINO.

🖥 Github: https://github.com/caoyunkang/segment-any-anomaly

🖥 Colab: https://colab.research.google.com/drive/1Rwio_KfziuLp79Qh_ugum64Hjnq4ZwsE?usp=sharing

⏩ Paper: https://arxiv.org/abs/2305.11013v1

📌 Dataset: https://paperswithcode.com/dataset/visa

https://t.iss.one/DataScienceT

Diff-Pruning: Structural Pruning for Diffusion Models

Structural Pruning for Diffusion Models.

🖥 Github: https://github.com/vainf/diff-pruning

⏩ Paper: https://arxiv.org/abs/2305.10924v1

📌 Dataset: https://paperswithcode.com/dataset/lsun

https://t.iss.one/DataScienceT

🔥 Here's a list of 32 datasets that you can go over the weekend:
https://datasciencedojo.com/blog/datasets-data-science-skills/

✅ More reaction = more projects

@CodeProgrammer ♥️

How to Encrypt and Decrypt Image Using Python | How to Encrypt any Image File Using Python
https://morioh.com/p/978e38a1f65b?f=5c21fb01c16e2556b555ab32

✅ More reaction = more projects

@CodeProgrammer ♥️

Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold

Paper:
https://arxiv.org/abs/2305.10973

Github:
https://github.com/XingangPan/DragGAN

Project page:
https://vcai.mpi-inf.mpg.de/projects/DragGAN/

https://t.iss.one/DataScienceT

🦙 LLM-Pruner: On the Structural Pruning of Large Language Models

Compress your LLMs to any size;

🖥 Github: https://github.com/horseee/llm-pruner

⏩ Paper: https://arxiv.org/abs/2305.11627v1

📌 Dataset: https://paperswithcode.com/dataset/piqa

https://t.iss.one/DataScienceT

Mask-Free Video Instance Segmentation

MaskFreeVIS, achieving highly competitive VIS performance, while only using bounding box annotations for the object state.

🖥 Github: https://github.com/SysCV/maskfreevis

⏩ Paper: https://arxiv.org/pdf/2303.15904.pdf

📌 Project: https://www.vis.xyz/pub/maskfreevis/

https://t.iss.one/DataScienceT