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Found 14 papers, 9 papers with code
Vision-LLMs Can Fool Themselves with Self-Generated Typographic Attacks
Furthermore, prior work's Typographic attacks against CLIP randomly sample a misleading class from a predefined set of categories.
CLAMP: Contrastive LAnguage Model Prompt-tuning
Our results show that LLMs can, indeed, achieve good image classification performance when adapted this way.
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.
MixtureGrowth: Growing Neural Networks by Recombining Learned Parameters
Newly grown layer weights are generated by using a new linear combination of existing templates for a layer.
ERM++: An Improved Baseline for Domain Generalization
We also explore the relationship between DG performance and similarity to pre-training data, and find that similarity to pre-training data distributions is an important driver of performance, but that ERM++ with stronger initializations can deliver strong performance even on dissimilar datasets. Code is released at https://github. com/piotr-teterwak/erm_plusplus.
Mind the Backbone: Minimizing Backbone Distortion for Robust Object Detection
We propose to use Relative Gradient Norm (RGN) as a way to measure the vulnerability of a backbone to feature distortion, and show that high RGN is indeed correlated with lower OOD performance.
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.
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.
Tune it the Right Way: Unsupervised Validation of Domain Adaptation via Soft Neighborhood Density
Unsupervised domain adaptation (UDA) methods can dramatically improve generalization on unlabeled target domains.
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.
OCONet: Image Extrapolation by Object Completion
Existing methods struggle to extrapolate images with salient objects in the foreground or are limited to very specific objects such as humans, but tend to work well on indoor/outdoor scenes.
Understanding Invariance via Feedforward Inversion of Discriminatively Trained Classifiers
We use our reconstruction model as a tool for exploring the nature of representations, including: the influence of model architecture and training objectives (specifically robust losses), the forms of invariance that networks achieve, representational differences between correctly and incorrectly classified images, and the effects of manipulating logits and images.
Supervised Contrastive Learning
Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models.
Ranked #2 on
Class Incremental Learning
on cifar100
Boundless: Generative Adversarial Networks for Image Extension
Image extension models have broad applications in image editing, computational photography and computer graphics.
Ranked #2 on
Uncropping
on Places2 val
