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Found 16 papers, 15 papers with code
GeNIe: Generative Hard Negative Images Through Diffusion
To achieve this, inspired by recent diffusion based image editing techniques, we limit the number of diffusion iterations to ensure the generated image retains low-level and background features from the source image while representing the target category, resulting in a hard negative sample for the source category.
Compact3D: Compressing Gaussian Splat Radiance Field Models with Vector Quantization
3D Gaussian Splatting is a new method for modeling and rendering 3D radiance fields that achieves much faster learning and rendering time compared to SOTA NeRF methods.
NOLA: Compressing LoRA using Linear Combination of Random Basis
These methods can reduce the number of parameters needed to fine-tune an LLM by several orders of magnitude.
SlowFormer: Universal Adversarial Patch for Attack on Compute and Energy Efficiency of Inference Efficient Vision Transformers
We show that such models can be vulnerable to a universal adversarial patch attack, where the attacker optimizes for a patch that when pasted on any image, can increase the compute and power consumption of the model.
SimA: Simple Softmax-free Attention for Vision Transformers
Recently, vision transformers have become very popular.
Backdoor Attacks on Vision Transformers
Vision Transformers (ViT) have recently demonstrated exemplary performance on a variety of vision tasks and are being used as an alternative to CNNs.
PRANC: Pseudo RAndom Networks for Compacting deep models
We demonstrate that a deep model can be reparametrized as a linear combination of several randomly initialized and frozen deep models in the weight space.
SimReg: Regression as a Simple Yet Effective Tool for Self-supervised Knowledge Distillation
Feature regression is a simple way to distill large neural network models to smaller ones.
Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning
On the other hand, far away NNs may not be semantically related to the query.
Adaptive Token Sampling For Efficient Vision Transformers
Since ATS is a parameter-free module, it can be added to the off-the-shelf pre-trained vision transformers as a plug and play module, thus reducing their GFLOPs without any additional training.
Ranked #13 on
Efficient ViTs
on ImageNet-1K (with DeiT-S)
Constrained Mean Shift for Representation Learning
Inspired by recent success of self-supervised learning (SSL), we develop a non-contrastive representation learning method that can exploit additional knowledge.
Consistent Explanations by Contrastive Learning
We show that our method, Contrastive Grad-CAM Consistency (CGC), results in Grad-CAM interpretation heatmaps that are more consistent with human annotations while still achieving comparable classification accuracy.
Backdoor Attacks on Self-Supervised Learning
We show that such methods are vulnerable to backdoor attacks - where an attacker poisons a small part of the unlabeled data by adding a trigger (image patch chosen by the attacker) to the images.
Mean Shift for Self-Supervised Learning
Most recent self-supervised learning (SSL) algorithms learn features by contrasting between instances of images or by clustering the images and then contrasting between the image clusters.
ISD: Self-Supervised Learning by Iterative Similarity Distillation
Hence, we introduce a self supervised learning algorithm where we use a soft similarity for the negative images rather than a binary distinction between positive and negative pairs.
CompRess: Self-Supervised Learning by Compressing Representations
To the best of our knowledge, this is the first time a self-supervised AlexNet has outperformed supervised one on ImageNet classification.
