Machine Learning with a Honk (mlhonk.substack.com)

Machine Learning with a Honk is a curated series by Massimiliano Viola that, in recent weeks, has tracked fast-moving trends across computer vision and generative models — from self‑supervised vision transformers to practical tooling for diffusion models and multimodal systems. Highlights include a deep dive into the DINO→DINOv3 evolution (self‑supervised ViTs improving representation quality and downstream transfer), a flagged flaw in Stable Diffusion’s VAE (potentially affecting latent fidelity), and practical recipes like Step1X‑Edit for building text‑guided image‑editing datasets. Other pieces show how diffusion models are being retooled beyond generation: DIFT extracts semantic and geometric correspondences from diffusion features, Marigold repurposes diffusion generators for dense prediction tasks, and IP‑Adapter gives pretrained text‑to‑image diffusion models image‑prompting ability. Collectively the posts map current technical priorities: stronger self‑supervised objectives (I‑JEPA and DINO variants) for label‑efficient learning; augmentation of diffusion models for editing, correspondence, and dense prediction; and expanding multimodal capabilities (LLaVA visual instruction tuning, SAM segmentation, DreamBooth/Textual Inversion for subject personalization). The coverage flags both opportunities (better generalization, new supervision signals, plug‑and‑play adapters) and risks (latent model flaws), making it a compact snapshot of where research and tooling are converging in vision and generative AI.