2020
15 min read
DDPM: Denoising Diffusion Probabilistic Models
How diffusion models learn to generate images by reversing a gradual noising process — the foundation of Stable Diffusion, DALL-E, and modern image generation.
Machine Learning Paper Reviews
Expert analysis and in-depth reviews of machine learning research papers. Covering computer vision, deep learning, and AI innovations with practical insights.
31
Paper Reviews
24
Topics Covered
2020-2024
Years Span
45
Unique Authors
All Paper Reviews
2023
15 min read
Flow Matching: Simplified Generative Modeling
How Flow Matching simplifies generative modeling by learning straight transport paths from noise to data — faster sampling, simpler training, and the foundation of modern generation systems.
2022
15 min read
Latent Diffusion Models: High-Resolution Image Synthesis
How Latent Diffusion Models made high-resolution image generation practical by moving diffusion to a compressed latent space — the architecture behind Stable Diffusion.
2022
15 min read
BEiT: BERT Pre-Training of Image Transformers
How BEiT bridges BERT and vision by predicting discrete visual tokens from masked image patches — the first masked image modeling approach for Vision Transformers, achieving 83.2% on ImageNet-1K.
2024
15 min read
DINOv2: Learning Robust Visual Features without Supervision
How DINOv2 combines DINO self-distillation with iBOT masked prediction at scale on curated data (LVD-142M), producing the strongest open-source frozen visual features across classification, segmentation, depth, and retrieval.
2023
15 min read
I-JEPA: Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture
How I-JEPA learns visual representations by predicting abstract feature representations of masked image regions — no pixel reconstruction, no augmentation — achieving 81.7% linear probe accuracy with ViT-H.
Showing 1-6 of 31 articles