Search Results for author:
Found 10 papers, 4 papers with code
SABAF: Removing Strong Attribute Bias from Neural Networks with Adversarial Filtering
To that end, in this work, we mathematically and empirically reveal the limitation of existing attribute bias removal methods in presence of strong bias and propose a new method that can mitigate this limitation.
Information-Theoretic Bounds on The Removal of Attribute-Specific Bias From Neural Networks
Ensuring a neural network is not relying on protected attributes (e. g., race, sex, age) for predictions is crucial in advancing fair and trustworthy AI.
Shadow Datasets, New challenging datasets for Causal Representation Learning
Most causal representation learning (CRL) methods are fully supervised, which is impractical due to costly labeling.
A Critical View of Vision-Based Long-Term Dynamics Prediction Under Environment Misalignment
In this paper, we investigate two challenging conditions for environment misalignment: Cross-Domain and Cross-Context by proposing four datasets that are designed for these challenges: SimB-Border, SimB-Split, BlenB-Border, and BlenB-Split.
SW-VAE: Weakly Supervised Learn Disentangled Representation Via Latent Factor Swapping
However, the training process without utilizing any supervision signal have been proved to be inadequate for disentanglement representation learning.
Weakly Supervised Invariant Representation Learning Via Disentangling Known and Unknown Nuisance Factors
Disentangled and invariant representations are two critical goals of representation learning and many approaches have been proposed to achieve either one of them.
Do-Operation Guided Causal Representation Learning with Reduced Supervision Strength
Causal representation learning has been proposed to encode relationships between factors presented in the high dimensional data.
Reconstruction for disentanglement, Contrast for invariance
Disentangled and invariant representation are two vital goals for representation learning and many approaches have been proposed to achieve one of them.
Partner-Assisted Learning for Few-Shot Image Classification
In this paper, we focus on the design of training strategy to obtain an elemental representation such that the prototype of each novel class can be estimated from a few labeled samples.
MUSCLE: Strengthening Semi-Supervised Learning Via Concurrent Unsupervised Learning Using Mutual Information Maximization
We also show that MUSCLE has the potential to boost the classification performance when used in the fine-tuning phase for a model pre-trained only on unlabeled data.
