Active learning for deep detection neural network.pdf paper : https://openaccess.thecvf.com/content_ICCV_2019/papers/Aghdam_Active_Learning_for_Deep_Detection_Neural_Networks_ICCV_2019_paper.pdf
Consistency-based Semi-supervised Learning for Object Detection.pdf paper : https://papers.nips.cc/paper/2019/file/d0f4dae80c3d0277922f8371d5827292-Paper.pdf
Detecting Unseen Visual Relations Using Analogies.pdf paper: https://openaccess.thecvf.com/content_ICCV_2019/papers/Peyre_Detecting_Unseen_Visual_Relations_Using_Analogies_ICCV_2019_paper.pdf
Extreme view synthesis.pdf paper : https://arxiv.org/abs/1812.04777
Learning Imbalanced Datasets with Label-Distribution-Aware?Margin Loss.pdf paper : https://proceedings.neurips.cc/paper/2019/file/621461af90cadfdaf0e8d4cc25129f91-Paper.pdf
NeurIPS-2019-learning-imbalanced-datasets-with-label-distribution-aware-margin-loss-Paper.pdf paper : http://papers.neurips.cc/paper/8435-learning-imbalanced-datasets-with-label-distribution-aware-margin-loss.pdf
PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments.pdf paper : http://www.robots.ox.ac.uk/~david/papers/novotny19perspectivenet.pdf
Region Mutual Information Loss for Semantic Segmentation.pdf paper : https://proceedings.neurips.cc/paper/2019/file/a67c8c9a961b4182688768dd9ba015fe-Paper.pdf
S4L: Self-Supervised Semi-Supervised Learning.pdf paper : https://openaccess.thecvf.com/content_ICCV_2019/papers/Zhai_S4L_Self-Supervised_Semi-Supervised_Learning_ICCV_2019_paper.pdf
Scaling and benchmarking self-supervised visual representation learning.pdf paper : https://research.fb.com/wp-content/uploads/2019/08/Scaling-and-Benchmarking-Self-Supervised-Visual-Representation-Learning.pdf
SILCO: Show a Few Images, Localize the Common Object.pdf paper : https://openaccess.thecvf.com/content_ICCV_2019/papers/Hu_SILCO_Show_a_Few_Images_Localize_the_Common_Object_ICCV_2019_paper.pdf
Stacked Capsule Autoencoders.pdf paper : https://arxiv.org/pdf/1906.06818.pdf
Symmetric cross entropy for robust learning with noisy labels.pdf paper : https://openaccess.thecvf.com/content_ICCV_2019/papers/Wang_Symmetric_Cross_Entropy_for_Robust_Learning_With_Noisy_Labels_ICCV_2019_paper.pdf
Tensormask: A foundation for dense object segmentation.pdf paper : https://openaccess.thecvf.com/content_ICCV_2019/papers/Chen_TensorMask_A_Foundation_for_Dense_Object_Segmentation_ICCV_2019_paper.pdf
Trust Region Based Adversarial Attack on Neural Networks.pdf paper : https://openaccess.thecvf.com/content_CVPR_2019/papers/Yao_Trust_Region_Based_Adversarial_Attack_on_Neural_Networks_CVPR_2019_paper.pdf
Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction.pdf paper : https://papers.nips.cc/paper/2019/file/801272ee79cfde7fa5960571fee36b9b-Paper.pdf
Unsupervised Video Interpolation Using Cycle Consistency.pdf paper : https://openaccess.thecvf.com/content_ICCV_2019/papers/Reda_Unsupervised_Video_Interpolation_Using_Cycle_Consistency_ICCV_2019_paper.pdf
A UNIFIED THEORY OF EARLY VISUAL?REPRESENTATIONS FROM RETINA TO?CORTEX THROUGH ANATOMICALLY?CONSTRAINED DEEP CNNS.pdf paper : https://arxiv.org/abs/1901.00945
Confidence Calibration for Convolutional Neural?Networks Using Structured Dropout.pdf paper : https://arxiv.org/abs/1906.09551
Generalized Cross Entropy Loss for Training Deep?Neural Networks with Noisy Labels.pdf paper : https://papers.nips.cc/paper/2018/file/f2925f97bc13ad2852a7a551802feea0-Paper.pdf
Generating High Fidelity Images with Sub-scale Pixel?Networks and Multidimensional Up-scaling.pdf paper : https://arxiv.org/abs/1812.01608
How does Batch Normalization Help Optimization?.pdf paper : https://arxiv.org/pdf/1805.11604.pdf
Interpretable Explanations of Black Boxes by?Meaningful Perturbation.pdf paper : http://www.robots.ox.ac.uk/~vedaldi/assets/pubs/fong17interpretable.pdf
Interpretable to whom? A role-based model for?analyzing interpretable Machine Learning systems.pdf paper : https://arxiv.org/pdf/1806.07552.pdf
Learning Robust representations by projecting?superficial statistics out.pdf paper : https://openreview.net/forum?id=rJEjjoR9K7
META-LEARNING UPDATE RULES FOR?UNSUPERVISED REPRESENTATION LEARNING.pdf paper : https://arxiv.org/abs/1804.00222
Mind the Gap: A Generative Approach to?Interpretable Feature Selection and Extraction.pdf paper : https://beenkim.github.io/papers/BKim2015NIPS.pdf
Norm matters: efficient and accurate normalization?schemes in deep networks.pdf paper : https://arxiv.org/abs/1803.01814
PAY LESS ATTENTION WITH LIGHTWEIGHT?AND DYNAMIC CONVOLUTIONS.pdf paper : https://openreview.net/pdf?id=SkVhlh09tX
RISE: Randomized Input Sampling for Explanation?of Black-box Models.pdf paper : https://arxiv.org/pdf/1806.07421.pdf
SATNet: Bridging Deep Learning and Logical?Reasoning using a Differentiable Satisfiability Solver.pdf paper : https://arxiv.org/pdf/1905.12149.pdf
THE LOTTERY TICKET HYPOTHESIS: FINDING?SPARSE, TRAINABLE NEURAL NETWORKS.pdf paper : https://arxiv.org/abs/1803.03635
THE NEURO-SYMBOLIC CONCEPT LEARNER:?INTERPRETING SCENES, WORDS, AND?SENTENCES FROM NATURAL SUPERVISION.pdf paper : https://arxiv.org/abs/1904.12584
Towards Understanding Learning Representations:?To What Extent Do Different Neural Networks Learn?the Same Representation.pdf paper : https://arxiv.org/pdf/1810.11750.pdf
Transferring Knowledge across Learning Processes.pdf paper : https://openreview.net/pdf?id=HygBZnRctX
Understanding and Utilizing Deep Neural Networks?Trained with Noisy Labels.pdf paper : https://arxiv.org/abs/1905.05040