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Found 12 papers, 9 papers with code
Learning to Compose SuperWeights for Neural Parameter Allocation Search
To address this, we generate layer weights by learning to compose sets of SuperWeights, which represent a group of trainable parameters.
Lasagna: Layered Score Distillation for Disentangled Object Relighting
Although generative editing methods now enable some forms of image editing, relighting is still beyond today's capabilities; existing methods struggle to keep other aspects of the image -- colors, shapes, and textures -- consistent after the edit.
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste Sorting
To test the abilities of computer vision models on this task, we present the VisDA 2022 Challenge on Domain Adaptation for Industrial Waste Sorting.
MaskSketch: Unpaired Structure-guided Masked Image Generation
We show that intermediate self-attention maps of a masked generative transformer encode important structural information of the input image, such as scene layout and object shape, and we propose a novel sampling method based on this observation to enable structure-guided generation.
Disentangled Unsupervised Image Translation via Restricted Information Flow
Unsupervised image-to-image translation methods aim to map images from one domain into plausible examples from another domain while preserving structures shared across two domains.
MixtureEnsembles: Leveraging Parameter Sharing for Efficient Ensembles
We improve on these methods with MixtureEnsembles, which learns to factorize ensemble members with shared parameters by constructing each layer with a linear combination of templates.
Compositional Models: Multi-Task Learning and Knowledge Transfer with Modular Networks
From practical perspective, our approach allows to: (a) reuse existing modules for learning new task by adjusting the computation order, (b) use it for unsupervised multi-source domain adaptation to illustrate that adaptation to unseen data can be achieved by only manipulating the order of pretrained modules, (c) show how our approach can be used to increase accuracy of existing architectures for image classification tasks such as ImageNet, without any parameter increase, by reusing the same block multiple times.
VisDA-2021 Competition Universal Domain Adaptation to Improve Performance on Out-of-Distribution Data
Progress in machine learning is typically measured by training and testing a model on the same distribution of data, i. e., the same domain.
ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes
Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets.
Evaluation of Correctness in Unsupervised Many-to-Many Image Translation
Given an input image from a source domain and a guidance image from a target domain, unsupervised many-to-many image-to-image (UMMI2I) translation methods seek to generate a plausible example from the target domain that preserves domain-invariant information of the input source image and inherits the domain-specific information from the guidance image.
Adversarial Self-Defense for Cycle-Consistent GANs
The goal of unsupervised image-to-image translation is to map images from one domain to another without the ground truth correspondence between the two domains.
Unsupervised Video-to-Video Translation
Unsupervised image-to-image translation is a recently proposed task of translating an image to a different style or domain given only unpaired image examples at training time.
