Pinned Repositories
AdCo
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries
AET
Auto-Encoding Transformations (AETv1), CVPR 2019
AVT-pytorch
Autoencoding Variational Transformations (AVT) in pytorch, ICCV 2019
CaCo
CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning
CapProNet-Pytorch
Capsule Projection Networks (CapProNet) in pytorch, NeurIPS 2018
CapProNet_tf
Capsule Projection Networks (CapProNet) in pytorch, NeurIPS 2018
CLSA
official implemntation for "Contrastive Learning with Stronger Augmentations"
EnAET
EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning
glsgan-gp
Generalized Loss-Sensitive Generative Adversarial Networks (GLS-GAN) in PyTorch with gradient penalty, including both LS-GAN and WGAN as special cases.
WCP
Source code for "WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning" in CPVR 2020.
Lab for MAchine Perception and LEarning (MAPLE)'s Repositories
maple-research-lab/AdCo
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries
maple-research-lab/AET
Auto-Encoding Transformations (AETv1), CVPR 2019
maple-research-lab/EnAET
EnAET: Self-Trained Ensemble AutoEncoding Transformations for Semi-Supervised Learning
maple-research-lab/CLSA
official implemntation for "Contrastive Learning with Stronger Augmentations"
maple-research-lab/CapProNet-Pytorch
Capsule Projection Networks (CapProNet) in pytorch, NeurIPS 2018
maple-research-lab/glsgan-gp
Generalized Loss-Sensitive Generative Adversarial Networks (GLS-GAN) in PyTorch with gradient penalty, including both LS-GAN and WGAN as special cases.
maple-research-lab/WCP
Source code for "WCP: Worst-Case Perturbations for Semi-Supervised Deep Learning" in CPVR 2020.
maple-research-lab/AVT-pytorch
Autoencoding Variational Transformations (AVT) in pytorch, ICCV 2019
maple-research-lab/CapProNet_tf
Capsule Projection Networks (CapProNet) in pytorch, NeurIPS 2018
maple-research-lab/CaCo
CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning
maple-research-lab/SIM
Inference-only implementation of "One-Step Diffusion Distillation through Score Implicit Matching" [NIPS 2024]
maple-research-lab/lsgan-gp-alt
Loss-Sensitive GAN with gradient penalty in tensorflow
maple-research-lab/EqDiff
Equilibrated Diffusion: Frequency-aware Textual Embedding for EquilibratedImage Customization. https://maple-aigc.github.io/EqDiff/
maple-research-lab/graph-ter
GraphTER: Unsupervised Learning of Graph Transformation Equivariant Representations via Auto-Encoding Node-wise Transformations, in Proceedings of IEEE/CVF Conferences on Computer Vision and Pattern Recognition (CVPR 2020)
maple-research-lab/AdPE
code for "AdPE: Adversarial Positional Embeddings for Pretraining Vision Transformers via MAE+"
maple-research-lab/TrGAN
Transformation GAN for Unsupervised Image Synthesis and Representation Learning, in Proceedings of IEEE/CVF Conferences on Computer Vision and Pattern Recognition (CVPR 2020)
maple-research-lab/CapProNet
This project is obsolete. New one is at https://github.com/maple-research-lab/CapProNet_tf.
maple-research-lab/glsgan
Generalized Loss-Sensitive GAN in torch, IJCV
maple-research-lab/lsgan
Loss-Sensitive Generative Adversarial Networks (LS-GAN) in torch, IJCV
maple-research-lab/sfm
State-Frequency Memory Recurrent Neural Networks, ICML 2017
maple-research-lab/CapProNet_pytorch
maple-research-lab/lsal-blocks
Generalized Loss-Sensitive Adversarial Learning implemented in Blocks
maple-research-lab/lsal-torch
Generalized Loss-Sensitive Adversarial Learning in torch