neurips-2022

[NeurIPS 2022 Spotlight] VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

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This is a collection of our zero-cost NAS and efficient vision applications.

[NeurIPS 2022] Implementation of "AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition"

Code for our NeurIPS 2022 paper

PyTorch Implementation of GenerSpeech (NeurIPS'22): a text-to-speech model towards zero-shot style transfer of OOD custom voice.

The official implementation of NeurIPS22 spotlight paper "NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification"

Implementation of Torsional Diffusion for Molecular Conformer Generation (NeurIPS 2022)

[NeurIPS2022] Deep Model Reassembly

HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details (NeurIPS 2022)

(NeurIPS 2022 CellSeg Challenge - 1st Winner) Open source code for "MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy"

[NeurIPS'22] Official code of "ComMU: Dataset for Combinatorial Music Generation"

[NeurIPS 2022] Official PyTorch implementation of Optimizing Relevance Maps of Vision Transformers Improves Robustness. This code allows to finetune the explainability maps of Vision Transformers to enhance robustness.

A Python 3 package for learning Bayesian Networks (DAGs) from data. Official implementation of the paper "DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization"

[NeurIPS 2022] The official implementation of "Learning to Discover and Detect Objects".

(NeurIPS 2022) Self-Supervised Visual Representation Learning with Semantic Grouping

Official Implementation for "HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks" (NeurIPS 2022)

(CVPR 2023) Official code of MACARONS: Mapping And Coverage Anticipation with RGB ONline Self-supervision. Also contains an updated and improved implementation of our previous work SCONE (NeurIPS 2022), on which this work is built.

[NeurIPS 2022 Spotlight] This is the official PyTorch implementation of "EcoFormer: Energy-Saving Attention with Linear Complexity"

Code for our NeurIPS 2022 (spotlight) paper 'Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation'