AI Image2025
Edits and generates images from natural-language prompts: blend photos, swap backgrounds, change a pose, or remove an object, keeping a person's or pet's face consistent across edits. Multi-turn refinement; outputs carry a SynthID watermark.
AI Image2022
Generates polished images and image-to-video animations, with web and Discord workflows for creating, editing, remixing, and exploring visual outputs. Best suited to aesthetic ideation rather than fully controllable production pipelines.
AI Video2024
Generates videos and images from text or reference images, with model updates aimed at higher motion realism and creator-friendly controls. Best for fast concept clips, ads, and social assets rather than fully predictable production footage.
AI Image2012
Provides a comprehensive set of computer-vision algorithms and image/video processing utilities with multi-language bindings (C++, Python, Java), contrib modules, and community docs/forums — suitable for prototyping, production pipelines, and real-time applications.
The result that kicked off the deep learning era: in 2012 a deep CNN cut ImageNet top-5 error from 26% to 15%, showing that GPU-trained networks with ReLU and dropout could beat decades of hand-engineered computer vision features.
Frames generative modeling as a two-player game: a generator forges data while a discriminator learns to spot fakes, training both by backpropagation alone — no Markov chains, no inference networks. The adversarial pressure yields sharp samples.
Stanford's course teaches deep learning by making you build vision models from scratch — k-NN and linear classifiers up through CNNs, detection, segmentation, and Transformers — with three PyTorch assignments and a self-chosen final project.
Introduced dilated (atrous) convolutions, which expand a filter's receptive field exponentially with no loss of resolution and no extra parameters — the trick that let dense-prediction networks see wide context while keeping per-pixel detail.