Search Results for author:
Found 19 papers, 8 papers with code
CyCADA: Cycle-Consistent Adversarial Domain Adaptation
Domain adaptation is critical for success in new, unseen environments.
Adversarial Discriminative Domain Adaptation
Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains.
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
We evaluate whether features extracted from the activation of a deep convolutional network trained in a fully supervised fashion on a large, fixed set of object recognition tasks can be re-purposed to novel generic tasks.
Unsupervised Domain Adaptation through Self-Supervision
This paper addresses unsupervised domain adaptation, the setting where labeled training data is available on a source domain, but the goal is to have good performance on a target domain with only unlabeled data.
Deep Domain Confusion: Maximizing for Domain Invariance
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark.
Ranked #6 on
Domain Adaptation
on Office-Caltech
LSDA: Large Scale Detection Through Adaptation
A major challenge in scaling object detection is the difficulty of obtaining labeled images for large numbers of categories.
Semantic Bottleneck Scene Generation
For the former, we use an unconditional progressive segmentation generation network that captures the distribution of realistic semantic scene layouts.
Ranked #1 on
Image Generation
on Cityscapes-5K 256x512
Simultaneous Deep Transfer Across Domains and Tasks
Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias.
Adapting Deep Visuomotor Representations with Weak Pairwise Constraints
We propose a novel, more powerful combination of both distribution and pairwise image alignment, and remove the requirement for expensive annotation by using weakly aligned pairs of images in the source and target domains.
Visual Discovery at Pinterest
Over the past three years Pinterest has experimented with several visual search and recommendation services, including Related Pins (2014), Similar Looks (2015), Flashlight (2016) and Lens (2017).
Human Curation and Convnets: Powering Item-to-Item Recommendations on Pinterest
This paper presents Pinterest Related Pins, an item-to-item recommendation system that combines collaborative filtering with content-based ranking.
One-Shot Adaptation of Supervised Deep Convolutional Models
In other words, are deep CNNs trained on large amounts of labeled data as susceptible to dataset bias as previous methods have been shown to be?
SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection
Without dense labels, as is the case when only detection labels are available in the source, transformations are learned using CycleGAN alignment.
Learning a Unified Embedding for Visual Search at Pinterest
The solution we present not only allows us to train for multiple application objectives in a single deep neural network architecture, but takes advantage of correlated information in the combination of all training data from each application to generate a unified embedding that outperforms all specialized embeddings previously deployed for each product.
Revisiting Few-shot Activity Detection with Class Similarity Control
In this paper, we present a conceptually simple and general yet novel framework for few-shot temporal activity detection based on proposal regression which detects the start and end time of the activities in untrimmed videos.
Unconditional Synthesis of Complex Scenes Using a Semantic Bottleneck
Coupling the high-fidelity generation capabilities of label-conditional image synthesis methods with the flexibility of unconditional generative models, we propose a semantic bottleneck GAN model for unconditional synthesis of complex scenes.
Toward Transformer-Based Object Detection
The Vision Transformer was the first major attempt to apply a pure transformer model directly to images as input, demonstrating that as compared to convolutional networks, transformer-based architectures can achieve competitive results on benchmark classification tasks.
Pyramid Mini-Batching for Optimal Transport
Optimal transport theory provides a useful tool to measure the differences between two distributions.
