This paper introduces PAWS, a method for semi-supervised learning that combines labeled and unlabeled data to train models efficiently. PAWS minimizes a consistency loss to ensure similar pseudo-labels for different views of the same image, leveraging labeled samples non-parametrically. It achieves state-of-the-art results on ImageNet with significantly less training time.
Barlow Twins is a self-supervised learning approach that leverages redundancy reduction to create embeddings invariant to input distortions. It uses two identical neural networks to minimize redundancy between vector components, ensuring robust representations. This method is competitive with state-of-the-art self-supervised learning techniques.
FaceNet is a face recognition system that maps face images to a compact Euclidean space for efficient face recognition and clustering tasks. It was developed by researchers at Google and is known for its high accuracy in face recognition benchmarks.
This book provides an in-depth exploration of how humans perceive depth, including topics such as depth contrast, stereopsis, and disparity. It is a valuable resource for those interested in visual psychology and neuropsychology.
SimCLR is a method for contrastive learning that maximizes agreement between differently augmented views of the same data example. It is used for self-supervised learning tasks in computer vision.
In 'Pause: A Study of Its Nature and Its Rhythmical Function in Verse, Especially Blank Verse', Ada Laura Fonda Snell explores the role of pauses in creating rhythm in poetry, particularly in blank verse. This work reflects her academic interest in the structural aspects of poetry and her use of innovative methods to analyze poetic rhythm.
This publication by the National Union of Teachers focuses on the issues of teacher turnover and the effects of the London Allowance. It presents a sample survey and analysis aimed at understanding the factors influencing teacher retention and the financial incentives provided by the London Allowance.
Dr. Ishan Misra is a Research Scientist at Facebook AI Research where he works on Computer Vision and Machine Learning. His main research interest is reducing the need for human supervision, and indeed, human knowledge in visual learning systems. He finished his PhD at the Robotics Institute at Carnegie Mellon. He has done stints at Microsoft Research, INRIA and Yale. His bachelors is in computer science where he achieved the highest GPA in his cohort.
Ishan is fast becoming a prolific scientist, already with more than 3000 citations under his belt and co-authoring with Yann LeCun; the godfather of deep learning. Today though we will be focusing an exciting cluster of recent papers around unsupervised representation learning for computer vision released from FAIR. These are; DINO: Emerging Properties in Self-Supervised Vision Transformers, BARLOW TWINS: Self-Supervised Learning via Redundancy Reduction and PAWS: Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with
Support Samples. All of these papers are hot off the press, just being officially released in the last month or so. Many of you will remember PIRL: Self-Supervised Learning of Pretext-Invariant Representations which Ishan was the primary author of in 2019.
References;
Shuffle and Learn - https://arxiv.org/abs/1603.08561
DepthContrast - https://arxiv.org/abs/2101.02691
DINO - https://arxiv.org/abs/2104.14294
Barlow Twins - https://arxiv.org/abs/2103.03230
SwAV - https://arxiv.org/abs/2006.09882
PIRL - https://arxiv.org/abs/1912.01991
AVID - https://arxiv.org/abs/2004.12943 (best paper candidate at CVPR'21 (just announced over the weekend) - http://cvpr2021.thecvf.com/node/290)
Alexei (Alyosha) Efros
http://people.eecs.berkeley.edu/~efros/
http://www.cs.cmu.edu/~tmalisie/projects/nips09/
Exemplar networks
https://arxiv.org/abs/1406.6909
The bitter lesson - Rich Sutton
http://www.incompleteideas.net/IncIdeas/BitterLesson.html
Machine Teaching: A New Paradigm for Building Machine Learning Systems
https://arxiv.org/abs/1707.06742
POET
https://arxiv.org/pdf/1901.01753.pdf
Machine Learning Street Talk (MLST) 
