OpenAI has designed its new neural network architecture CLIP (Contrastive Language-Image Pretraining) for Learning Transferable Visual Models From Natural Language Supervision.
In a recent work by Microsoft Research, a new framework is introduced which can address in the case of unforeseen corruptions or distribution shifts of data models to create “unadversarial objects,” inputs that are optimized particularly for more robust model performance.
The Adversarial Robustness Toolbox(ART) is a Python library which is one of the complete resources providing developers and researchers for evaluating the robustness of deep neural networks against adversarial attacks
Sktime is a unified python framework/library providing API for machine learning with time series data and sklearn compatible tools to analyse, visualize, tune and validate multiple time series learning models such as time series forecasting, time series regression and classification.
TrainGenerator is a Streamlit based web app for machine learning template code generation surpassing the different stages of data loading, preprocessing, model development, hyperparameter setting, and declaring other such constraints for complete model building.
In this article, we will discuss the various image datasets that are readily available for training machine learning models.
WILDS is a benchmark of in-the-wild distribution shifts spanning a variety of datasets and applications, consisting of wildlife monitoring, tumour identification, poverty mapping and some others.
This article contains data annotation tools and at the end, there is a comprehensive table for guidance to services and solutions provided by each
SVoice is Facebook Research’s newly achieved state-of-the-art speech separation technique for multiple voices speaking simultaneously in a single audio sequence
ArtLine uses deep learning algorithms to achieve fine quality line art portraits, movie posters and cartoonize images.
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding containing annotations for per pixel ground truth labels and corresponding ground truth geometry, material information, and lighting information for every scene.
VIBE – Video Inference for 3D Human Body Pose and Shape Estimation. It uses CNNs, RNNs(GRU) and GANs along with a self-attention layer to achieve its state-of-the-art results.
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