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Found 18 papers, 15 papers with code
Hierarchical Context Merging: Better Long Context Understanding for Pre-trained LLMs
Large language models (LLMs) have shown remarkable performance in various natural language processing tasks.
S-CLIP: Semi-supervised Vision-Language Learning using Few Specialist Captions
By combining these objectives, S-CLIP significantly enhances the training of CLIP using only a few image-text pairs, as demonstrated in various specialist domains, including remote sensing, fashion, scientific figures, and comics.
RoPAWS: Robust Semi-supervised Representation Learning from Uncurated Data
Semi-supervised learning aims to train a model using limited labels.
Diffusion Probabilistic Models for Structured Node Classification
This paper studies structured node classification on graphs, where the predictions should consider dependencies between the node labels.
Discovering and Mitigating Visual Biases through Keyword Explanation
The keyword explanation form of visual bias offers several advantages, such as a clear group naming for bias discovery and a natural extension for debiasing using these group names.
OAMixer: Object-aware Mixing Layer for Vision Transformers
Using the object labels, OAMixer computes a reweighting mask with a learnable scale parameter that intensifies the interaction of patches containing similar objects and applies the mask to the patch mixing layers.
Breaking the Spurious Causality of Conditional Generation via Fairness Intervention with Corrective Sampling
(a) Fairness Intervention (FI): emphasize the minority samples that are hard to generate due to the spurious correlation in the training dataset.
Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks
In this paper, we found that the recent emerging paradigm of implicit neural representations (INRs) that encodes a continuous signal into a parameterized neural network effectively mitigates the issue.
Ranked #25 on
Video Generation
on UCF-101
Object-aware Contrastive Learning for Debiased Scene Representation
Contrastive self-supervised learning has shown impressive results in learning visual representations from unlabeled images by enforcing invariance against different data augmentations.
Abstract Reasoning via Logic-guided Generation
Abstract reasoning, i. e., inferring complicated patterns from given observations, is a central building block of artificial general intelligence.
MASKER: Masked Keyword Regularization for Reliable Text Classification
We claim that one central obstacle to the reliability is the over-reliance of the model on a limited number of keywords, instead of looking at the whole context.
Layer-adaptive sparsity for the Magnitude-based Pruning
Recent discoveries on neural network pruning reveal that, with a carefully chosen layerwise sparsity, a simple magnitude-based pruning achieves state-of-the-art tradeoff between sparsity and performance.
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances
Based on this, we propose a new detection score that is specific to the proposed training scheme.
Freeze the Discriminator: a Simple Baseline for Fine-Tuning GANs
Generative adversarial networks (GANs) have shown outstanding performance on a wide range of problems in computer vision, graphics, and machine learning, but often require numerous training data and heavy computational resources.
Ranked #5 on
10-shot image generation
on Babies
Lookahead: a Far-Sighted Alternative of Magnitude-based Pruning
Magnitude-based pruning is one of the simplest methods for pruning neural networks.
Mining GOLD Samples for Conditional GANs
Conditional generative adversarial networks (cGANs) have gained a considerable attention in recent years due to its class-wise controllability and superior quality for complex generation tasks.
Instance-aware Image-to-Image Translation
Unsupervised image-to-image translation has gained considerable attention due to the recent impressive progress based on generative adversarial networks (GANs).
InstaGAN: Instance-aware Image-to-Image Translation
Our comparative evaluation demonstrates the effectiveness of the proposed method on different image datasets, in particular, in the aforementioned challenging cases.
