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Found 7 papers, 3 papers with code
Selective Mixup Fine-Tuning for Optimizing Non-Decomposable Objectives
We find that current state-of-the-art empirical techniques offer sub-optimal performance on these practical, non-decomposable performance objectives.
Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics
Using the proposed CSST framework, we obtain practical self-training methods (for both vision and NLP tasks) for optimizing different non-decomposable metrics using deep neural networks.
Towards Achieving Adversarial Robustness Beyond Perceptual Limits
The presence of images that flip Oracle predictions and those that do not, makes this a challenging setting for adversarial robustness.
Cross-Conditioned Recurrent Networks for Long-Term Synthesis of Inter-Person Human Motion Interactions
Modeling dynamics of human motion is one of the most challenging sequence modeling problem, with diverse applications in animation industry, human-robot interaction, motion-based surveillance, etc.
Crafting Data-free Universal Adversaries with Dilate Loss
We introduce a method to create Universal Adversarial Perturbations (UAP) for a given CNN in a data-free manner.
Game of Sketches: Deep Recurrent Models of Pictionary-style Word Guessing
Similarly, performance on multi-disciplinary tasks such as Visual Question Answering (VQA) is considered a marker for gauging progress in Computer Vision.
DeLiGAN : Generative Adversarial Networks for Diverse and Limited Data
A class of recent approaches for generating images, called Generative Adversarial Networks (GAN), have been used to generate impressively realistic images of objects, bedrooms, handwritten digits and a variety of other image modalities.
