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Found 13 papers, 5 papers with code
Challenges of Adversarial Image Augmentations
Image augmentations applied during training are crucial for the generalization performance of image classifiers.
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.
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.
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.
Kanerva++: extending The Kanerva Machine with differentiable, locally block allocated latent memory
Episodic and semantic memory are critical components of the human memory model.
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.
Self-Supervised MultiModal Versatile Networks
In particular, we explore how best to combine the modalities, such that fine-grained representations of the visual and audio modalities can be maintained, whilst also integrating text into a common embedding.
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.
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].
Continual Classification Learning Using Generative Models
Continual learning is the ability to sequentially learn over time by accommodating knowledge while retaining previously learned experiences.
A New Benchmark and Progress Toward Improved Weakly Supervised Learning
Knowledge Matters: Importance of Prior Information for Optimization [7], by Gulcehre et.
Large-scale Nonlinear Variable Selection via Kernel Random Features
We propose a new method for input variable selection in nonlinear regression.
Lifelong Generative Modeling
Lifelong learning is the problem of learning multiple consecutive tasks in a sequential manner, where knowledge gained from previous tasks is retained and used to aid future learning over the lifetime of the learner.
