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This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a framework for aligning feature distributions between source and target datasets, a toolkit for domain generalization, and a library for semi-supervised learning using small labeled datasets and large unlabeled sets. The library provides specialized capabilities for unsupervised domain adaptation, including the use of adversarial networks, discrepancy-based architectures, and image-to-image translation to reduce distribution mismatch. It also includes tools for domain generali
This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy of machine learning models. It provides a standardized collection of labeled fashion product images and training data formatted to be compatible with the MNIST dataset structure. The dataset consists of fixed-dimension grayscale images and label-based category mappings, stored in a binary format. It includes pre-split training and testing sets and a static distribution to ensure consistent cross-model benchmarking. The repository supports image classification benchmarking and
Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018
CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns
Code for the paper "Adversarial Feature Augmentation for Unsupervised Domain Adaptation", CVPR 2018
The main features of ricvolpi/adversarial-feature-augmentation are: Adversarial Adaptation Methods, Computer Vision Research.
Open-source alternatives to ricvolpi/adversarial-feature-augmentation include: thuml/transfer-learning-library — This project is a comprehensive library for transfer learning and domain adaptation in computer vision. It serves as a… zalandoresearch/fashion-mnist — This project is a computer vision benchmark and image classification dataset used to measure and compare the accuracy… agrimgupta92/sgan — Code for "Social GAN: Socially Acceptable Trajectories with Generative Adversarial Networks", Gupta et al, CVPR 2018. aimerykong/recurrent-pixel-embedding-for-instance-grouping — CVPR2018 - pixel embedding & grouping for structured prediction, e.g., instance segmentation. akanazawa/cmr — Angjoo Kanazawa \ , Shubham Tulsiani \ , Alexei A. Efros, Jitendra Malik. ahangchen/tfusion — CVPR2018: Unsupervised Cross-dataset Person Re-identification by Transfer Learning of Spatio-temporal Patterns.