Sascha KirchinTowards Data ScienceThe Rise of Diffusion Models — A new Era of Generative Deep LearningDenoising Diffusion Probabilistic Models by J. Ho et. al.Mar 272Mar 272
Sascha KirchinTowards Data ScienceDepth Anything —A Foundation Model for Monocular Depth EstimationDepth Anything: Unleashing the Power of Large-Scale Unlabeled Data by L. Yang et. al.Mar 201Mar 201
Sascha KirchinTowards Data ScienceTurn Yourself into a 3D Gaussian SplatA Hands-on Guide for PractitionersMar 147Mar 147
Sascha KirchinTowards Data ScienceDINO — A Foundation Model for Computer VisionEmerging Properties in Self-Supervised Vision Transformers by M. Caron et. al.Sep 27, 20231Sep 27, 20231
Sascha KirchinTowards Data ScienceSegment Anything — Promptable Segmentation of Arbitrary ObjectsSegment Anything by A. Krillov et. al.Sep 14, 20232Sep 14, 20232
Sascha KirchinTowards Data ScienceBYOL -The Alternative to Contrastive Self-Supervised LearningBootstrap Your Own Latent: A New Approach to Self-Supervised Learning by J. Grill et. al.Sep 7, 20232Sep 7, 20232
Sascha KirchinTowards Data ScienceGLIP: Introducing Language-Image Pre-Training to Object DetectionGrounded Language-Image Pre-training by L. H. Li et. al.Sep 1, 2023Sep 1, 2023
Sascha KirchinTowards Data ScienceThe CLIP Foundation ModelLearning Transferable Visual Models From Natural Language Supervision by A. Radford et. al.Aug 26, 2023Aug 26, 2023
Sascha KirchinTowards Data ScienceImplement Multi-GPU Training on a single GPUAn Advanced Guide for TensorFlowMay 15, 2023May 15, 2023
Sascha KirchinTowards Data ScienceFourier CNNs with Kernel Sizes of 1024x1024 and LargerMulti-Dimensional Fourier Transformations in Convolutional Neural NetworksMay 31, 2022May 31, 2022
