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Found 9 papers, 2 papers with code
INDIGO: Intrinsic Multimodality for Domain Generalization
To this end, we propose IntriNsic multimodality for DomaIn GeneralizatiOn (INDIGO), a simple and elegant way of leveraging the intrinsic modality present in these pre-trained multimodal networks along with the visual modality to enhance generalization to unseen domains at test-time.
Context-Conditional Adaptation for Recognizing Unseen Classes in Unseen Domains
Recent progress towards designing models that can generalize to unseen domains (i. e domain generalization) or unseen classes (i. e zero-shot learning) has embarked interest towards building models that can tackle both domain-shift and semantic shift simultaneously (i. e zero-shot domain generalization).
Data InStance Prior (DISP) in Generative Adversarial Networks
Previous works have addressed training in low data setting by leveraging transfer learning and data augmentation techniques.
Data Instance Prior for Transfer Learning in GANs
Recent advances in generative adversarial networks (GANs) have shown remarkable progress in generating high-quality images.
On Saliency Maps and Adversarial Robustness
A Very recent trend has emerged to couple the notion of interpretability and adversarial robustness, unlike earlier efforts which solely focused on good interpretations or robustness against adversaries.
On the benefits of defining vicinal distributions in latent space
The vicinal risk minimization (VRM) principle is an empirical risk minimization (ERM) variant that replaces Dirac masses with vicinal functions.
Attributional Robustness Training using Input-Gradient Spatial Alignment
Safe deployment of machine learning system mandates that the prediction and its explanation be reliable and robust.
Ranked #1 on
Weakly-Supervised Object Localization
on CUB-200-2011
(Top-1 Error Rate metric)
BIG-bench Machine Learning
Weakly-Supervised Object Localization
AdvGAN++ : Harnessing latent layers for adversary generation
Adversarial examples are fabricated examples, indistinguishable from the original image that mislead neural networks and drastically lower their performance.
Charting the Right Manifold: Manifold Mixup for Few-shot Learning
A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution.
