Lab for MAchine Perception and LEarning (MAPLE)
Lab for MAchine Perception and LEarning (MAPLE)
AdCo
AdCo: Adversarial Contrast for Efficient Learning of Unsupervised Representations from Self-Trained Negative Adversaries
AVT-pytorch
Autoencoding Variational Transformations (AVT) in pytorch, ICCV 2019
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.
CaCo
CaCo: Both Positive and Negative Samples are Directly Learnable via Cooperative-adversarial Contrastive Learning