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Found 11 papers, 11 papers with code
Few-shot Learner Parameterization by Diffusion Time-steps
To this end, we find an inductive bias that the time-steps of a Diffusion Model (DM) can isolate the nuanced class attributes, i. e., as the forward diffusion adds noise to an image at each time-step, nuanced attributes are usually lost at an earlier time-step than the spurious attributes that are visually prominent.
Exploring Diffusion Time-steps for Unsupervised Representation Learning
Representation learning is all about discovering the hidden modular attributes that generate the data faithfully.
Proposal-Level Unsupervised Domain Adaptation for Open World Unbiased Detector
This is because the predictor is inevitably biased to the known categories, and fails under the shift in the appearance of the unseen categories.
Invariant Feature Regularization for Fair Face Recognition
Unfortunately, face datasets inevitably capture the imbalanced demographic attributes that are ubiquitous in real-world observations, and the model learns biased feature that generalizes poorly in the minority group.
Make the U in UDA Matter: Invariant Consistency Learning for Unsupervised Domain Adaptation
Domain Adaptation (DA) is always challenged by the spurious correlation between domain-invariant features (e. g., class identity) and domain-specific features (e. g., environment) that does not generalize to the target domain.
Random Boxes Are Open-world Object Detectors
First, as the randomization is independent of the distribution of the limited known objects, the random proposals become the instrumental variable that prevents the training from being confounded by the known objects.
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly Detection
At each MIL training iteration, we use the current detector to divide the samples into two groups with different context biases: the most confident abnormal/normal snippets and the rest ambiguous ones.
Self-Supervised Learning Disentangled Group Representation as Feature
A good visual representation is an inference map from observations (images) to features (vectors) that faithfully reflects the hidden modularized generative factors (semantics).
Transporting Causal Mechanisms for Unsupervised Domain Adaptation
However, the theoretical solution provided by transportability is far from practical for UDA, because it requires the stratification and representation of the unobserved confounder that is the cause of the domain gap.
Counterfactual Zero-Shot and Open-Set Visual Recognition
We show that the key reason is that the generation is not Counterfactual Faithful, and thus we propose a faithful one, whose generation is from the sample-specific counterfactual question: What would the sample look like, if we set its class attribute to a certain class, while keeping its sample attribute unchanged?
Interventional Few-Shot Learning
Specifically, we develop three effective IFSL algorithmic implementations based on the backdoor adjustment, which is essentially a causal intervention towards the SCM of many-shot learning: the upper-bound of FSL in a causal view.
