Search Results for "learning"
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Showing 11 open source projects for "learning"
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Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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ImageReward
[NeurIPS 2023] ImageReward: Learning and Evaluating Human Preferences
...Beyond evaluation, ImageReward supports Reward Feedback Learning (ReFL), a method for directly fine-tuning diffusion models such as Stable Diffusion using human-preference feedback, leading to demonstrable improvements in image quality. -
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Photoshot
An open-source AI avatar generator web app
Photoshot is an AI-powered image generation and editing tool that enables users to create and modify images using advanced machine learning techniques. It allows users to generate realistic portraits, edit existing photos, and apply AI-based enhancements with minimal manual effort. -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). ... -
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OpenAI DALL·E AsyncImage SwiftUI
OpenAI swift async text to image for SwiftUI app using OpenAI
...In machine learning, diffusion models, also known as diffusion probabilistic models, are a class of latent variable models. They are Markov chains trained using variational inference. The goal of diffusion models is to learn the latent structure of a dataset by modeling the way in which data points diffuse through the latent space. -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
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Data Image Augment Generator
Dataset Image Augmentation Generator is a desktop application
...Color Adjustments: Brightness, Contrast, Color Jitter, Gamma Correction, Channel Shuffle, Posterize 4. Advanced Techniques: Elastic Deformation, Cutout, CLAHE, Edge Enhancement, Histogram Equalization, Fourier Noise 5. Deep Learning Methods: Mixup, CutMix, Random Occlusion Target Audience 1. Machine Learning Engineers 2. Data Scientists 3. Computer Vision Researchers 4. Students learning ML/CV 5. Anyone -
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Intelligent Java
Integrate with the latest language models, image generation and speech
Intelligent java (IntelliJava) is the ultimate tool to integrate with the latest language models and deep learning frameworks using java. The library provides an intuitive functions for sending input to models like ChatGPT and DALL·E, and receiving generated text, speech or images. With just a few lines of code, you can easily access the power of cutting-edge AI models to enhance your projects. Access ChatGPT, GPT3 to generate text and DALL·E to generate images. -
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texturize
Generate photo-realistic textures based on source images
...It's useful in the context of computer graphics if you want to make variations on a theme or expand the size of an existing texture. This software is powered by deep learning technology, using a combination of convolution networks and example-based optimization to synthesize images. We're building texturize as the highest-quality open source library available! The examples are available as notebooks, and you can run them directly in-browser thanks to Jupyter and Google Colab. -
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Deep Daze
Simple command line tool for text to image generation
Simple command-line tool for text to image generation using OpenAI's CLIP and Siren (Implicit neural representation network). In true deep learning fashion, more layers will yield better results. Default is at 16, but can be increased to 32 depending on your resources. Technique first devised and shared by Mario Klingemann, it allows you to prime the generator network with a starting image, before being steered towards the text. Simply specify the path to the image you wish to use, and optionally the number of initial training steps. ... -
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PyTTI-Notebook
PyTTI-Notebook
Recent advances in machine learning have created opportunities for “AI” technologies to assist unlocking creativity in powerful ways. PyTTI is a toolkit that facilitates image generation, animation, and manipulation using processes that could be thought of as a human artist collaborating with AI assistants. The underlying technology is complex, but you don’t need to be a deep learning expert or even know coding of any kind to use these tools. -
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PaddleGAN
PaddlePaddle GAN library, including lots of interesting applications
...GAN-Generative Adversarial Network, was praised by "the Father of Convolutional Networks" Yann LeCun (Yang Likun) as [One of the most interesting ideas in the field of computer science in the past decade]. It's the one research area in deep learning that AI researchers are most concerned about. -
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VQGAN-CLIP web app
Local image generation using VQGAN-CLIP or CLIP guided diffusion
VQGAN-CLIP has been in vogue for generating art using deep learning. Searching the r/deepdream subreddit for VQGAN-CLIP yields quite a number of results. Basically, VQGAN can generate pretty high-fidelity images, while CLIP can produce relevant captions for images. Combined, VQGAN-CLIP can take prompts from human input, and iterate to generate images that fit the prompts. Thanks to the generosity of creators sharing notebooks on Google Colab, the VQGAN-CLIP technique has seen widespread circulation. ...
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