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Found 11 papers, 3 papers with code
Introducing v0.5 of the AI Safety Benchmark from MLCommons
We created a new taxonomy of 13 hazard categories, of which 7 have tests in the v0. 5 benchmark.
All Should Be Equal in the Eyes of Language Models: Counterfactually Aware Fair Text Generation
Fairness in Language Models (LMs) remains a longstanding challenge, given the inherent biases in training data that can be perpetuated by models and affect the downstream tasks.
Towards Estimating Transferability using Hard Subsets
As transfer learning techniques are increasingly used to transfer knowledge from the source model to the target task, it becomes important to quantify which source models are suitable for a given target task without performing computationally expensive fine tuning.
Distilling the Undistillable: Learning from a Nasty Teacher
The inadvertent stealing of private/sensitive information using Knowledge Distillation (KD) has been getting significant attention recently and has guided subsequent defense efforts considering its critical nature.
One-Shot Doc Snippet Detection: Powering Search in Document Beyond Text
MONOMER fuses context from visual, textual, and spatial modalities of snippets and documents to find query snippet in target documents.
On Conditioning the Input Noise for Controlled Image Generation with Diffusion Models
Conditional image generation has paved the way for several breakthroughs in image editing, generating stock photos and 3-D object generation.
SAC: Semantic Attention Composition for Text-Conditioned Image Retrieval
The ability to efficiently search for images is essential for improving the user experiences across various products.
Retrospective Loss: Looking Back to Improve Training of Deep Neural Networks
Deep neural networks (DNNs) are powerful learning machines that have enabled breakthroughs in several domains.
SieveNet: A Unified Framework for Robust Image-Based Virtual Try-On
An efficient framework for this is composed of two stages: (1) warping (transforming) the try-on cloth to align with the pose and shape of the target model, and (2) a texture transfer module to seamlessly integrate the warped try-on cloth onto the target model image.
Retrospection: Leveraging the Past for Efficient Training of Deep Neural Networks
Deep neural networks are powerful learning machines that have enabled breakthroughs in several domains.
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.
