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Found 16 papers, 11 papers with code
Understanding and Estimating the Adaptability of Domain-Invariant Representations
We also propose a method for estimating how well a model based on domain-invariant representations will perform on the target domain, without having seen any target labels.
Fairy: Fast Parallelized Instruction-Guided Video-to-Video Synthesis
In this paper, we introduce Fairy, a minimalist yet robust adaptation of image-editing diffusion models, enhancing them for video editing applications.
The Inductive Bias of Flatness Regularization for Deep Matrix Factorization
Recent works on over-parameterized neural networks have shown that the stochasticity in optimizers has the implicit regularization effect of minimizing the sharpness of the loss function (in particular, the trace of its Hessian) over the family zero-loss solutions.
Debiasing Vision-Language Models via Biased Prompts
Machine learning models have been shown to inherit biases from their training datasets.
InfoOT: Information Maximizing Optimal Transport
Optimal transport aligns samples across distributions by minimizing the transportation cost between them, e. g., the geometric distances.
Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks
Understanding generalization and robustness of machine learning models fundamentally relies on assuming an appropriate metric on the data space.
Robust Contrastive Learning against Noisy Views
Contrastive learning relies on an assumption that positive pairs contain related views, e. g., patches of an image or co-occurring multimodal signals of a video, that share certain underlying information about an instance.
Measuring Generalization with Optimal Transport
Understanding the generalization of deep neural networks is one of the most important tasks in deep learning.
Fair Mixup: Fairness via Interpolation
Training classifiers under fairness constraints such as group fairness, regularizes the disparities of predictions between the groups.
Contrastive Learning with Hard Negative Samples
How can you sample good negative examples for contrastive learning?
Estimating Generalization under Distribution Shifts via Domain-Invariant Representations
When machine learning models are deployed on a test distribution different from the training distribution, they can perform poorly, but overestimate their performance.
Debiased Contrastive Learning
A prominent technique for self-supervised representation learning has been to contrast semantically similar and dissimilar pairs of samples.
The Role of Embedding Complexity in Domain-invariant Representations
In this work, we study, theoretically and empirically, the effect of the embedding complexity on generalization to the target domain.
Learning to Act Properly: Predicting and Explaining Affordances from Images
We address the problem of affordance reasoning in diverse scenes that appear in the real world.
Show, Adapt and Tell: Adversarial Training of Cross-domain Image Captioner
The domain critic assesses whether the generated sentences are indistinguishable from sentences in the target domain.
Leveraging Video Descriptions to Learn Video Question Answering
Then, a large number of candidate QA pairs are automatically generated from descriptions rather than manually annotated.
