Search Results for author:
Found 8 papers, 5 papers with code
You Don't Need Data-Augmentation in Self-Supervised Learning
Self-Supervised learning (SSL) with Joint-Embedding Architectures (JEA) has led to outstanding performances.
Automatic Data Curation for Self-Supervised Learning: A Clustering-Based Approach
This manual process has some limitations similar to those encountered in supervised learning, e. g., the crowd-sourced selection of data is costly and time-consuming, preventing scaling the dataset size.
Better (pseudo-)labels for semi-supervised instance segmentation
Despite the availability of large datasets for tasks like image classification and image-text alignment, labeled data for more complex recognition tasks, such as detection and segmentation, is less abundant.
DINOv2: Learning Robust Visual Features without Supervision
The recent breakthroughs in natural language processing for model pretraining on large quantities of data have opened the way for similar foundation models in computer vision.
Ranked #1 on
Image Retrieval
on AmsterTime
(using extra training data)
Code Translation with Compiler Representations
Here we propose to augment code translation with IRs, specifically LLVM IR, with results on the C++, Java, Rust, and Go languages.
DOBF: A Deobfuscation Pre-Training Objective for Programming Languages
Recent advances in self-supervised learning have dramatically improved the state of the art on a wide variety of tasks.
Continuous Surface Embeddings
In this work, we focus on the task of learning and representing dense correspondences in deformable object categories.
Putting Self-Supervised Token Embedding on the Tables
Information distribution by electronic messages is a privileged means of transmission for many businesses and individuals, often under the form of plain-text tables.
