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Found 13 papers, 4 papers with code
Poly-View Contrastive Learning
Contrastive learning typically matches pairs of related views among a number of unrelated negative views.
Elastic Weight Consolidation Improves the Robustness of Self-Supervised Learning Methods under Transfer
Self-supervised representation learning (SSL) methods provide an effective label-free initial condition for fine-tuning downstream tasks.
Do Self-Supervised and Supervised Methods Learn Similar Visual Representations?
Despite the success of a number of recent techniques for visual self-supervised deep learning, there has been limited investigation into the representations that are ultimately learned.
Stochastic Contrastive Learning
While state-of-the-art contrastive Self-Supervised Learning (SSL) models produce results competitive with their supervised counterparts, they lack the ability to infer latent variables.
Evaluating the fairness of fine-tuning strategies in self-supervised learning
In this work we examine how fine-tuning impacts the fairness of contrastive Self-Supervised Learning (SSL) models.
Hypersim: A Photorealistic Synthetic Dataset for Holistic Indoor Scene Understanding
To create our dataset, we leverage a large repository of synthetic scenes created by professional artists, and we generate 77, 400 images of 461 indoor scenes with detailed per-pixel labels and corresponding ground truth geometry.
Relational Mimic for Visual Adversarial Imitation Learning
In this work, we introduce a new method for imitation learning from video demonstrations.
Improving Discrete Latent Representations With Differentiable Approximation Bridges
Modern neural network training relies on piece-wise (sub-)differentiable functions in order to use backpropagation to update model parameters.
Mirroring to Build Trust in Digital Assistants
We describe experiments towards building a conversational digital assistant that considers the preferred conversational style of the user.
Variational Saccading: Efficient Inference for Large Resolution Images
Image classification with deep neural networks is typically restricted to images of small dimensionality such as 224 x 244 in Resnet models [24].
A New Benchmark and Progress Toward Improved Weakly Supervised Learning
Knowledge Matters: Importance of Prior Information for Optimization [7], by Gulcehre et.
Learning from Simulated and Unsupervised Images through Adversarial Training
With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.
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Image-to-Image Translation
on Cityscapes Labels-to-Photo
(Per-class Accuracy metric)
