Official Implementation for "Platypose: Calibrated Zero-Shot Multi-Hypothesis 3D Human Motion Estimation"

PhD thesis template for Uni Tübingen

Unified MultiWOZ evaluation scripts for the context-to-response task.

A PyTorch implementation of MAGE: MAsked Generative Encoder to Unify Representation Learning and Image Synthesis

PyTorch implementation of neural style randomization for data augmentation

This is the official code for calibration in multi-hypothesis human pose estimation

Virtual whiteboard for sketching hand-drawn like diagrams

PyTorch implementation of the U-Net for image semantic segmentation with high quality images

Implementation of DALL-E 2, OpenAI's updated text-to-image synthesis neural network, in Pytorch

Awesome Knowledge-Distillation. 分类整理的知识蒸馏paper(2014-2021)。

A 1D analogue of the MNIST dataset for measuring spatial biases and answering Science of Deep Learning questions.

The code for performing MTL on object recognition with neural data

Instructions and examples to deploy some PyTorch code on slurm using a Singularity Container

A simple tool to update bib entries with their official information (e.g., DBLP or the ACL anthology).

The uncompromising Python code formatter

PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.

Evaluate three types of task shifting with popular continual learning algorithms.

Code base for "Generalization in data-driven models of primary visual cortex", Lurz et al. 2020

A Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, and JAX

A highly efficient implementation of Gaussian Processes in PyTorch

Pytorch implementations of Bayes By Backprop, MC Dropout, SGLD, the Local Reparametrization Trick, KF-Laplace, SG-HMC and more

Numpy implementation of Gaussian Process Regression

Best transfer learning and domain adaptation resources (papers, tutorials, datasets, etc.)

Code, data and benchmark from the paper "Unmasking the Inductive Biases of Unsupervised Object Representations for Video Sequences".

A toolbox for domain adaptation and semi-supervised learning. Contributions welcome.

Lucid library adapted for PyTorch

Code, data and benchmark from the paper "Benchmarking Robustness in Object Detection: Autonomous Driving when Winter is Coming" (NeurIPS 2019 ML4AD)