A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code
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Updated
Jan 28, 2022
A curated list of the latest breakthroughs in AI by release date with a clear video explanation, link to a more in-depth article, and code
Deep learning PyTorch library for time series forecasting, classification, and anomaly detection (originally for flood forecasting).
[SOTA] [92% acc] 786M-8k-44L-32H multi-instrumental music transformer with true full MIDI instruments range, efficient encoding, octo-velocity and outro tokens
[ECCV2024] DualDn: Dual-domain Denoising via Differentiable ISP. ⭐😤⭐💀 SOTA model for real-image denoising. 💀 Both in terms of denoising performance and generalization ability.
ICML 2024, Official Implementation of "Cross-view Masked Diffusion Transformers for Person Image Synthesis."
IEEE Transactions on Affective Computing, 2022
CAPTCHA project for Machine Learning Course https://arxiv.org/pdf/2006.11373.pdf
Researchers who published code, models (in some cases), and demo apps (in few cases) along with their SOTA paper
Official Repository for different models based on MultiHead VGAEs.
Image-Scene-Classification with 30 different classes.
The repository contains the project of Advanced Machine Learning: Visual Geolocalization: mapping images to GPS. It has been forked from the implementation of a SOTA paper. My team pushed it furher
Computer Vision: State of the Art model implementation using PyTorch framework.
Models and examples built with TensorFlow.
Contributions to ML tasks in the form of Tools, Videos , Notebooks, Apps and APIs
ORON is a state-of-the-art Digital Assistant that is backed by Machine-Learning.
MB-ORES: A Multi-Branch Object Reasoner for Visual Grounding in Remote Sensing
Step by step approach to build Convolution Neural Network as per State of the Art Model.
"Plant Disease Identification for Improved Agriculture" presents a curated selection of CNN models, including AlexNet, DenseNet121, and EfficientNetB0, achieving up to 99.94% accuracy in detecting plant diseases through image analysis. This repository demonstrates the power of deep learning combined with ensemble techniques to enhance precision.
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